Economics as Classical Mechanics

Personal income distribution in the US

2012-01-29T15:55:00.002+01:00

We are going to revisit our model for personal income distribution, PID. It was first formalized in 2003 and used income distributions through 2001. We had to convert all reports published by the Census Bureau in pdf format between 1947 and 1993 into excel tables. It took a month of hand work together with proof reading. These reports are not converted into digital format yet.

In 2006, we used new data (through 2005) and re-estimated the model. In 2010, we published a book on personal income distribution using data through 2008. It is a good time to refresh the model and evaluate its performance since 2001 with ten more years of data. All major results will be presented in this blog.

We start with presenting original data. The distribution of personal incomes since 1994 is characterized by a higher resolution – income bins are only $2500 wide. Our model assumes that the overall income distribution depends on the age pyramid and the level of real GDP per capita. However, the evolution of PID is slow and at a twenty year horizon one actually sees a frozen PID. The frozen PID results in an almost constant Gini ratio over time, which is actually reported by the Census Bureau.

We illustrate PID in a few figures below. Figure1 presents all PID published since 1994 between $0 and $100,000 as they are. We have included all people without income into the bin between $0 and $2500. One can observe that the number of people in higher income bins increases with time as well as the number of people with incomes above $100,000 shown in Figure 2. The portion of people with incomes above $100,000 has been increasing by 0.3% per year since 1994. Figure 3 shows the number of people with income above $100,000 as a function of work experience. The fastest growth is observed for the groups between 30 and 40 years of work experience, i.e. between 45 and 55 years of age.

Figure 4 depicts the population density functions, PDFs, for the years between 1994 and 2010. First, the estimates presented in Figure 1 were normalized to the total population for a given year. Then we reduced the income scale for individual years, i.e. from 1995 to 2010, by the total growth of real GDP. This allows normalizing the curves to the total income, i.e. we reduce all scales to that of 1994. Finally, we normalize the portions of populations in given bins to their widths for individual years and obtain the population density functions. Figure 4 proves that the distribution of personal incomes has not been changing over time in relative terms, i.e. a given portion of population always has a given portion of total income. From the PIDs one can always build the relevant Lorenz curves and estimate Gini ratios. For higher incomes, the distribution has to be described by the Pareto distribution. Figure 5 shows that the PDFs at higher incomes do follow a common power law with an exponent of -3.9.

Our first assessment of the income data obtained after 2001 is that they do follow up the previously obtained relationships. We expect that our model for personal income distribution should perform well.

Figure 1. Personal income distributions from 1994 to 2010.

Figure 2. Portion of people with income above $100,000. The portion increases by 0.3% per year.

Figure 3. The number of people with income above $100,000 as a function of work experience. The fastest growth is observed for the groups between 30 and 40 years of work experience, i.e. between 45 and 55 years of age.

Figure 4. Population density function, i.e. the number of people in a given bin normalized to the total number of people and the width of income bin, as a function of income reduced by the overall GDP growth.

Figure 5. The Pareto distribution at higher incomes.

Krugman on the current slump

2012-01-28T21:27:00.002+01:00

Paul Krugman has presented a graph with real GDP for the UK. It illustrates that the current crisis is worse than it was in 1929. I’ve also downloaded data from the Maddison historical data and the Conference Board total economic database, which has inherited the Groningen (read Maddison) database.

The idea was to compare GDP per capita estimates for the same periods in order to remove the effect of population growth. Surprisingly, I’ve got a different result. Figure 1 below demonstrates that the current evolution of real GDP in the UK is much better than after 1929. Moreover, the estimates of per capita GDP after 1929 show a deeper recession than in 2007. Unlike Krugman, I do not add GDP projections for 2012 to 2014.

In Italy, real GDP has a deeper fall after 2007 than after 1929 (Figure 2) but the estimates of real GDP per capita say that the quick recovery of real GDP was definitely driven by increasing population. The state of per capita curve in 2010 was very close to that in 1932, i.e. three years after start of the crisis.

Figure 1. The evolution of real GDP and GDP per capita in the UK after the 1929 and 2007 crisis.

Figure 2. The evolution of real GDP and GDP per capita in Italy after the 1929 and 2007 crisis.

On wise monetary policy and the absence of liquidity trap in Japan

2012-01-28T18:28:00.000+01:00

We have been following inflation in Japan since 2005 when our first paper on the Japanese economy was published. We have revisited inflation in Japan in 2010 and confirmed the predictions of deflation as expressed by the negative GDP deflator. In this blog, we also reported on deflation (both CPI and GDP deflator) several times. Here we validate our predictions of the rate of consumer price inflation (CPI) by the estimate for 2011. The Japan Bureau of Statistics has estimated the rate of CPI inflation as -0.3%.

The case of Japan is the best illustration of our concept linking inflation to the change in labour force. We assume that there was neither liquidity trap in Japan nor mistakes in monetary policy. The evolution of inflation is completely driven by the change in labour force. This is an unfortunate situation for Japan since the level of labour force can only fall in the long run due to the quickly decreasing working age population.

Previously, we carried out an estimation of empirical relationship between the change rate of labour force, dLF(t)/LF(t), and inflation, p(t). First, we test the existence of a link between inflation and labour force. Because of the structural (measurement related?) break in the 1980s, we have chosen the period after 1982 for linear regression. By varying the lag between the labour force and inflation one can obtain the best-fit coefficients for the prediction of CPI inflation, p(t), according to the following relationship:

p(t) = 1.43dLF(t-t₀)/LF(t-t₀) + 0.000 (1)

where the time lag t₀=0 years; standard errors for both coefficients are shown in brackets. Figure 1 (upper panel) depicts this best-fit case. There is no time lag between the inflation series and the labour force change series in Japan. Free term in (1), defining the level of price inflation in the absence of labour force change, is practically undistinguishable from zero.

A more precise and reliable method to compare observed and predicted inflation consists in the comparison of cumulative curves. Short-term oscillations and uncorrelated noise in data as induced by inaccurate measurements and the inevitable bias in all definitions should be smoothed out in cumulative curves. Any actual deviation between two cumulative curves persists in time if measured values are not matched by the defining relationship.

The predicted cumulative values shown in the lower panel of Figure 1 are very sensitive to the free term in (1). For Japan, the cumulative curves are characterized by complex shapes. There are periods of intensive inflation and a deflationary period. The labour force change, defining the predicted inflation curve, follows all the turns in the measured cumulative inflation.

One can conclude that relationship (1) is valid and the labour force change is the driving force of inflation. Statistically, the evolution of the overall level of consumer prices in Japan is fully defined by the change in labour force. Even the annual curves have Rsq=0.73 with all fluctuations induced by the change in labor force. The cumulative curves are characterized by Rsq=0.99. Hence, no other variable or process can affect the change in price. Otherwise, the statistically reliable link would not exist.

Effectively, this means that the Japanese monetary authorities can not create conditions for positive inflation and thus there is no liquidity trap. The problem of deflation can be resolved only in the framework of increasing population and Figure 3 shows that the next forty years will be characterized by price deflation (both CPI adn GDP deflator) when population projections are used to extrapolated labour force.

Figure 1. Measured inflation (CPI) and that predicted from the change rate of labour force. Upper panel: Annual curves. Lower panel: Cumulative curves between 1982 and 2009. A good agreement between the cumulative curves illustrates the predictive power of our model.

Figure 2. Scatter plot: predicted vs. measured rate of CPI inflation.

Figure 3. Inflation projection for Japan: CPI and the GDP deflator

Why we insist that personal income inequality does not change

2012-01-28T10:03:00.001+01:00

We have already reported that the personal income distribution in the USA does not change with time when normalized to the total population and total income. In other words, the relative distribution of personal income in the United States has not been changing since the start of income measurements in 1947. The accuracy of early measurements is not good enough, however, and we have to rely of the most recent results.

The US Census Bureau routinely reports income estimates obtained during the Annual Social and Economic Supplement of the Current Population Surveys. We have retrieved the population distribution over mean income in the range from $0 to $250,000 which is available only from 2000. The relevant measurements of the number of people in a given income range were carried out in $2500 bins between $0 and $100,000 and $50000 bins between $100,000 and $250,000. In order to suppress the influence of the width we have calculated the population density, i.e. the ratio of the number of people in a given bin and its width. The personal income is measured in current dollars and thus we have to reduce all incomes by the total change of the GDP deflator (one may also use CPI which gives a 20 per cent higher inflation rate) since 2000 to a given year. Figure 1 shows the result of normalization for 2000, 2005, and 2010. In relative terms, the income distribution has not been changing since 2000. At higher incomes, all three curves are practically identical. This observation is validated by the estimates of Gini ratio provided by the Census Bureau.

Figure 1. The population density function, PDF, as a function of mean income as normalized to the total personal income for a given year. At higher incomes, the curves are practically identical.

Two more graphs on GDP in the USA

2012-01-27T22:16:00.000+01:00

Two more graphs on real GDP in the USA. In the forth quarter of 2011, the level of real GDP was higher ($13,422 .4 billion) than that in the fourth quarter of 2007 ($13,326 billion) as Figure 1 shows. Figure 2 demonstrates that the increasing population did not allow real GDP per capita t reach the level of 2007: $42,727 vs. $43,791. In seems to be the task for 2012 to 2014 if no recession will occur.

Figure 1. Real GDP

Figure 2. Real GDP per capita

Real GDP and GDP deflator in 2011

2012-01-27T20:11:00.000+01:00

Here are some quick notes on the new estimates of real GDP and GDP deflator for 2011. Figurer 1 shows the rate of growth of real GDP, dlnGDP/dt, at annual and quarterly basis. In 2011, the rate is 0.017 1/y, i.e. 1.7% per year, despite the rate growth in the fourth quarter of 2.7% (SAAR). Previously, we predicted a small recession in 2012 and 2013. This prediction will be updated soon when the 2010 census results are published and incorporated into the so called postcensal population estimates.

Figure 2 shows the growth of total population for the purpose of per head calculations. Please notice a large step in population between 1999 and 2000 as associated with the error of closure, i.e. the difference between intercensal estimate for 2000 and the number enumerated in the 2000 census. Figure 3 presents the rate of growth of real GDP per capita , dlnG/dt. In 2011, the rate of growth was 0.0098 1/y. This means that the total increase in population was of 0.7%.

Figure 4 shows the GDP deflator or price inflation associated with the economy as a whole. This is the most comprehensive measure of inflation as we discussed many times in this blog. For 2011, the GDP deflator is 2.1%. This is larger than we predicted but the last quarter signals about upcoming deflation, as we foresaw six years ago. Currently, the FRB also foresees very low inflation rates through 2014.

According to our concept of GDP growth, real GDP per capita has a inertial component which is expressed in a constant annual increment, dG=const. Figure 5 updates the graph showing the evolution of real GDP per capita in the USA. One can observe a gradual return to the constant level of annual increment. This works as inertial movement in physics. However, one can expect some more years of dG less than average, dG<$490 (2011 US dollars). It is worth noting, that there is no output gap when real GDP per cpaita is considered. The evolution of dG exactly follows it long term trend and the years after 2007 serve to return dG to the trend from its highs in the late 1990s.

We have to notice that the estimates of the nominal GDP and GDP deflator are subject to revision which may be as high as several per cent (+2.1% for 2001). However, the long term trends in all presented variables fit our concept and predictions.

Figure 1. The growth rate of real GDP: annual and quarterly (annualized). MA(4) for the quarterly time series. For 2011, dlnGDP/dt=0.017 1/y.

Figure 2. The evolution of total resident population. Notice the jump between 1999 and 2000 – the closure error.

Figure 3. The growth rate of real GDP per capita. For 2011, the rate is (dlnG/dt=) 0.0098 1/y.

Figure 4. Annual and quarterly (annualized) price deflator of GDP. In the last quarter of 2011 the GDP deflator dropped to 0.004 1/y. This is likely a turn to deflation.

Figure 5. The increment of real GDP per capita since 1950. As predicted, the trend returns to a zero slope. There is no output gap.

Unemployment in Spain will be increasing further

2012-01-27T11:09:00.000+01:00

Here we revisit the rate of unemployment, ut, in Spain using its dependence on the change in labor force, lt=dLF/LFdt. There is a new estimate of 22.8% for the unemployment rate in 2011. In May 2011, we quantitatively predicted that this rate should only be growing. It may reach 29% if the link between the rate of unemployment and the rate of labor force change is correct, as has been observed since 1980.

Previously, it was found that Spain is characterized by the same relationship between unemployment and labor force as other developed countries. For Spain, we used data provided by the OECD. Figure 1 depicts unemployment and the change rate of labor force between 1960 and 2011. In line with the OECD description of the breaks in the labor force series:

Series breaks: In 2005, changes in the questionnaire and the implementation of CATI system in the field work affected the estimates. The 2005 questionnaire produced an additional increase of employment (132 000) and a decrease of unemployment (78 000). From 2001, the new unemployment definition established by the European Commission in 2000 has been introduced. From 1994, persons employed in the “Guardia Civil” are not included in the armed forces. As an indication, this category represented 59 600 people in 1994. In 1976, the lower age limit for inclusion in the Labour Force Survey was raised from 14 to 16, at the same time other modifications to the survey were introduced.

there are two spikes in the dLF/LF series near 1976 and 2001 as related to step revisions to the level. The spike around 1988 has no explanation in terms of the revisions to labor force, but is of the same amplitude. One can not exclude the opportunity that this spike is related to the processes of joining the EU in 1986.

As expected, the same functional form of dependence is valid for Spain. The estimation method is based on trial-and-error approach and seeks for the fit between annual curves. The final model is as follows

ut = -7.0lt + 0.31; t>1986

Figure 2 depicts observed and predicted curves. Before 1986, the curves diverge and a different model is likely holds. Because of high-amplitude oscillations in the original time series for the rate of labour force change, lt, we have to smooth it by MA(3). For the period after 1986, R2=0.7. Thus, the change in labor force has been driving the rate of unemployment in Spain. The negative coefficient implies that unemployment is Spain goes down when labor force starts to increase.

As has been predicted by our model, the rate of unemployment has increased in 2011. This is not the end of the sad story on unemployment in Spain. Figure 2 evidences that it will likely be growing further with the decreasing labor force.

Figure 1. Unemployment rate, u, and the rate of labor force change, l, in Spain according to the definition introduced by the OECD.

Figure 2. Prediction of inflation by labor force. Due to high variation in the estimates of labor force we have smoothed it with MA(3). For the observed and predicted curves, R2=0.7 for the period between 1986 and 2011.

Wal-Mart share in 2012

2012-01-26T18:29:00.000+01:00

We estimated our price model for Wal-Mart Stores (NYSE: WMT) nine months ago. The model is based on the decomposition of a share price into a sum of two selected consumer price indices. This is a new model defined by the (seasonally not adjusted) index of hospital and related services (HOSP) and the price index of miscellaneous personal services (MISS), as reported by the US BLS. The former CPI component leads the share price by 10 months and the latter one evolves in sync with the price. Figure 1 depicts the overall evolution of both involved indices through December 2011. A very specific feature of both indices is their linearity over time: they are close to straight lines.

In this post, we re-estimate the WMT share price using new data through December 2011. This allows validating the initial model and demonstrating its reliability. The previously obtained defining components are the same and provide the best fit model between June 2010 and March 2010 with only one month change in the lag for the HOST index. All coefficients in (1) are only slightly different for the new model (see below). The slope of the time trend is negative. The best-fit 2-C model for WMT(t) is as follows:

WMT(t) = 0.50HOSP(t-10) + 1.42MISS(t) - 28.39(t-1990) – 158.12 (January 2011) (1)

WMT(t) = 0.46HOSP(t-9) + 1.49MISS(t) - 28.03(t-1990) – 165.50 (March 2011)

WMT(t) = 0.46HOSP(t-9) + 1.30MISS(t) - 26.06(t-1990) – 141.92 (December 2011)

where t is calendar time. The predicted curve in Figure 2 evolves in sync with the observed price. The residual error is $2.13 for the period between June 2003 and December 2011. With both indices growing along their respective trends one can expect a slight increase to the level of $60 to $65 per share in 2012Q1. Figure 3 presents the residual model error.

Figure 1. Evolution of the price of HOSP and MISS.

Figure 2. Observed and predicted WMT share prices.

Figure 3. Residual error of the model.

Quarterly report: Loews share price model

2012-01-26T18:09:00.000+01:00

This is a quarterly report on the performance of our share price model for Loews Corporation (NYSE: L). The model is based on the decomposition into a weighted sum of two consumer price indices (selected from a larger number of CPIs), linear trend and constant, all coefficients and time lags to be estimated by a LSQ procedure. Here we test the previous model and make a regular update using new data. All in all, the original model is valid since October 2008 and does not show any sign of future changes. This is a reliable model valid during the past 50 months!

A preliminary model for Loews Corp. was obtained in September 2009 and covered the period from October 2008. This old model included the index of food without beverages (FB) and the index of transportation service (TS). The most recent model also used the monthly closing prices as of April 2011 and the CPI estimates published on April 14, 2011. The defining indices were almost the same: the index of food (F) and the TS index. Figure 1 depicts the evolution of the indices which provide the best fit model, i.e. the lowermost RMS residual error, between July 2003 and December 2011. The F index leads by 5 months and the TS index by 4 months. When new data through December 2011 are used, the model does not show any tangible change - only coefficients have been slightly drifting:

L(t) = -2.03F(t-5) – 2.12TS(t-4) +28.23(t-1990) + 448.98, March 2011

L(t) = -2.01F(t-5) – 2.09TS(t-4) +27.96(t-1990) +440.65, September 2011

L(t) = -2.03F(t-5) – 2.02TS(t-4) +27.65(t-1990) +431.99, December 2011

where L(t) is the share price in US dollars, t is calendar time. The new model is depicted in Figure 2 together with high and low monthly prices as a proxy to the uncertainty bound of the share price. The predicted curve leads the observed one by 4 months. The residual error is of $2.42 for the period between July 2003 and December 2011. In the first quarter of 2012, the model foresees essentially no change. It is worth noting that the model obtained in March 2011, accurately predicted the small fall observed in the second and third quarters of 2011.

Figure 1. Evolution of the price indices F and TS.

Figure 2. Observed and predicted share prices.

Why the Economic Projections of Federal Reserve Board are inconsistent

2012-01-26T11:45:00.001+01:00

The FRB members have recently projected the evolution of key macroeconomic variables including real GDP and the rate of unemployment. In our blog , we have developed a very accurate model linking the rate of unemployment in the US to the rate of real GDP (per capita) growth: (A series of posts has resulted in a working paper.) The following relationship was estimated:

du = -0.62dlnG + 1.09, (1)

When integrated between t0 and t, equation (1) can be rewritten in the following form:

u(t) = u(t0) + bln[G/G0] +a(t-t0) + c (2)

Without loss of generality, we assume t0=0. The intercept c≡0, as is clear for t=t0. Instead of integrating (2), we calculate cumulative sums of the annual estimates of du and lnG with appropriate initial conditions. The cumulative sum of du’s is the time series of the unemployment rate. Figure 1 depicts the measured and observed curves for the period between 1958 and 2011. The agreement is excellent and has been obtained by a formal statistical method (LSQR).

From (1) it follows that higher rates of GDP growth decrease the rate of unemployment. The FRB has projected real GDP with the highest rates of 2.7% in 2012, 3.2% in 2013, and 4% in 2014. We reduce these rates by 1% per year to estimate the per capita rate of growth, i.e. the growth in population is 1% per year. Using (2) we calculate the rate of uneployment which will correspond to the projected real GDP.

Figure 1 also depicts these predicted rates for 2012 to 2014 by open circles. The rates of unemployment projected by the FRB are shown by red circles. There is a significant deviation between the predicted and projected rates, which likely manifests the inconsistency in the FRB member's models of unemployment.

One may check these projections in 2015.

Figure 1. The observed and predicted rate of unemployment in the USA between 1958 and 2010.The projected rate of unemployment (middle point of the projections) is shown by red circles.

Who is responsible for income inequality? Blame old men.

2012-01-26T09:39:00.000+01:00

I've plotted a series of mean personal income estimates borrowed from the Census Bureau. There are 10-year age bins with data from 1967 for men and women separately. The first plot shows that the male mean income is much higher than that of female. The gap between them has been slightly decreasing since 1974 but not spectacularly. Therefore, a higher rate of income growth for women results in decreasing income inequality, i.e. convergence of mean incomes.
In order to highlight the age and sex groups growing at the the highest rates all mean values in given age groups are normalized to 1967 (except the youngest group normalized to 1974). These plot show the following empirical results:
1. Young women (especially between 25 and 44) have been effectively closing the income gap with men.
2. Younger male groups (from 15 to 34) have suffered absolute decrease in mean income since 1974!
3. For men, the highest rates of income growth belongs to the eldest group. Hence, one has to blame them for increasing inequality. Some of them are blogging on inequality.

Income inequality paradox - family vs. personal median income

2012-01-25T13:35:00.002+01:00

One can always find a good graph to illustrate increasing income inequality in the US. Lane Kenworthy and then Paul Krugman have demonstrated a dramatic deviation between the median family income and GDP per capita. We have already posted on the problems behind the definition of income and GDP. Here we illustrate another paradox of the data published by the US Census Bureau. From the same source we retrieved the following time series: median personal and family income (chained $), GDP per capita (chained $) and Gini ratios for family and personal income distribution. Figure 1 shows the median personal and family income as normalized to 1974, and the GDP per capita time series also normalized to 1974. This is practically the plot from Lane Kenworthy except the personal income median. Instructively, both median curves are very similar with small deviations likely associated with the estimation procedure than with actual changes.

Figure 1. Median incomes and GDP per capita in the US.

Now we plot another values also published by the Census Bureau. Figure 2 depicts Gini ratios for family and personal income distribution as calculated by the Bureau.

Figure 2. Gini ratios for personal and family income distributions measured by the Census Bureau.

A big surprise - Gini ratio for personal income has been decreasing since 1994 (no estimates before this point). So, the deviation between the GDP per capita and median income does not support increasing inequality. More likely, it is something wrong with the Census Bureau.

A model for Harley-Davidson share price: three years of success

2012-01-21T09:09:00.001+01:00

This is a quarterly report on the performance of our pricing model. Harley-Davidson (HOG) is one of the best illustrations of our concept (see a brief description of the concept in Appendix) linking stock prices to CPI components. For HOG, the model is stable for many years. The first model was obtained in September 2009 and covered the period from October 2008. Here we revisit the HOG model using the monthly closing price for December 2001 and the CPI estimates published in January 2012.

For HOG, the defining indices are as follows: the index of rent of primary residence (RPR) and the index of owners' equivalent rent of residence (ORPR). Both CPI components are leading the share price. Figure 1 depicts the evolution of the indices which provide the best fit model, i.e. the lowermost RMS residual error, between July 2008 and December 2011. The models are as follows:

HOG(t) = -13.82RPR(t-3) +12.77ORPR(t-4) +17.82(t-1990) – 163.94, before September 2009

HOG(t) = -11.30RPR(t-3) + 9.83ORPR(t-3) +17.53(t-1990) – 36.34, after September 2009

HOG(t) = -11.27RPR(t-3) + 9.55ORPR(t-3) +19.35(t-1990) – 8.57, September 2011

HOG(t) = -11.09RPR(t-4) + 9.40ORPR(t-4) +18.93(t-1990) – 7.53, December 2011

where HOG(t) is the share price in US dollars, t is calendar time. The model is characterised by standard deviation of $4.28 for the period between July 2003 and December 2011.

Two recent models are depicted in Figure 2. The predicted curve in December 2011 leads the observed ones by 4 months. We do foresee a further fall in the stock price to $26 per share in the first quarter of 2012. Figure 3 displays the residual error.

Figure 1. Evolution of the price indices ORPR and RPR.

Figure 2. Observed and predicted HOG share prices. Model for March, September, and December 2011.

Figure 3. The model residual error.

Appendix

In its general form, our pricing model is as follows:

sp(t_j) = Σb_i∙CPI_i(t_j-t_i) + c∙(t_j-1990 ) + d + e_j (1)

where sp(t_j) is the share price at discrete (calendar) times t_j, j=1,…,J; CPI_i(t_j-t_i) is the i-th component of the CPI with the time lag t_i, i=1,..,I; b_i, c and d are empirical coefficients of the linear and constant term; e_j is the residual error, which statistical properties have to be scrutinized. By definition, the bets-fit model minimizes the RMS residual error. The time lags are expected because of the delay between the change in one price (stock or goods and services) and the reaction of related prices. It is a fundamental feature of the model that the lags in (1) may be both negative and positive. In this study, we limit the largest lag to eleven months. Apparently, this is an artificial limitation and might be changed in a more elaborated model.

System (1) contains J equations for I+2 coefficients. For POM we use a time series from July 2003 to March 2011, i.e. 94 monthly readings. Due to the negative effects of a larger set of defining CPI components their number for all models is (I=) 2. To resolve the system, we use standard methods of matrix inversion. As a rule, solutions of (1) are stable with all coefficients far from zero. In the POM model, we use 92 CPI components. They are not seasonally adjusted indices and were retrieved from the database provided by the Bureau of Labor Statistics.

Due to obvious reasons, longer time series guarantee a better resolution between defining CPIs. In general, there are two sources of uncertainty associated with the difference between observed and predicted prices. First, we have taken the monthly close prices (adjusted for splits and dividends) from a large number of recorded prices: monthly and daily open, close, high, and low prices, their combinations as well as averaged prices. Second source of uncertainty is related to all kinds of measurement errors and intrinsic stochastic properties of the CPI and its components. One should also bear in mind all uncertainties associated with the CPI definition based on a fixed basket of goods and services, which prices are tracked in few selected places. Such measurement errors are directly mapped into the model residual errors. Both uncertainties, as related to stocks and CPI, also fluctuate from month to month.

Quarterly report: the performance of share price model for Hewlett Packard

2012-01-21T08:50:00.001+01:00

This is quarterly report of the performance of our share price model. Hewlett Packard (NYSE:HPQ) provides a good example of a successful share price prediction at a several month horizon. We have already published our predictions at a four month horizon four times (July 2010, January 2011, March 2011, July 2011, and September 2011). All predictions were based on our concept of share pricing as decomposition into a weighted sum of two CPI components. We calculated the evolution of the monthly closing price (adjusted for dividends and splits). Here we test and update the model using data through December 2011. The model is still accurate and robust.

Originally, the long term model for HPQ share price was defined by the index of food without beverages (FB) and that of rent of primary residency (RPR). The former CPI component led the share price by 4 months and the latter one led by 5 months. Figure 1 depicts the overall evolution of both involved indices through December 2011 (this is the reason of the time lead increase by 1 month relative to previous models where CPI were not contemporaneous). Below we present four best-fit 2-C models for HPQ(t) obtained at different times:

HPQ(t) = -3.20FB(t-4) + 2.91RPR(t-5) + 3.64(t-1990) - 50.82, July 2010

HPQ(t) = -3.34FB(t-4) + 3.41RPR(t-5) + 0.51(t-1990) - 85.44, June 2011

HPQ(t) = -3.46FB(t-4) + 3.68RPR(t-5) – 0.72(t-1990) - 99.88, September 2011

HPQ(t) = -3.40FB(t-5) + 3.60RPR(t-6) – 0.57(t-1990) – 97.72, December 2011

where HPQ(t) is the price in US dollars, t is calendar time. All coefficients have been slightly drifting but very close. This process expresses the trade-off between the linear trend in the difference between the defining CPIs and the time trend term in the above equtions.

The predicted curves are shown in Figure 2 (March, September, and December 2011). From Figure 2, we predict the price to fall to $20 in the first quarter of 2012 and then rise to $25 in Q2.

Figure 1. Evolution of the price of FB and RPR.

Figure 2. Observed and predicted HPQ share prices in March, September, and December 2011. The contemporaneous prediction is shown by solid red line. High and low prices are shown by dashed lines.

Quarterly report on Procter and Gamble

2012-01-20T14:15:00.001+01:00

This is a quarterly update of the share price model for Procter and Gamble as based on the decomposition into a weighted sum of two consumer price indices (to be determined), linear time trend and constant. It is shown that the model is valid since September 2009 at least and does not show any sign of possible failure. It predicts the share price at a four month horizon.

A share price model for Procter and Gamble (NYSE: PG) was originally published in this blog in July 2010. According to our concept, it was defined by the index of food away from home (SEFV - CUUS0000SEFV) and that of rent of primary residency (RPR); the evolution of these indices is presented in Figure 1. In the original model, the former CPI component led the share price by 3 months and the latter one led by 8 months. The upper panel of Figure 2 depicts the original model and the monthly closing prices available in July 2010. This original model was stable for the previous 11 months, i.e. for the period from September 2009.

In April 2011, we updated the original model using some new data (closing price for March 2011) and found that the same model was also applicable with a small change in the time lead for the SEFV – it was 4 months instead of 3 months in the original model. New coefficients were also slightly different, but very close to the original ones.

The September’s update uses the monthly closing price for September 2011 and CPIs for August 2011. It validates the model obtained for the previous period but is characterized by the same time lags and a small shift in the coefficients estimated by the LSQ technique. The current update for the December 2011 closing price is obtained using the CPI data for December 2011 and thus both time shifts are one month longer. Four best-fit models for PG(t) are as follows:

PG(t) = -5.88SEFV(t-3) + 3.43RPR(t-8) + 17.60(t-1990) + 174.08, July 2010

PG(t) = -5.40SEFV(t-4) + 2.93RPR(t-8) + 18.16(t-1990) + 187.47, March 2011

PG(t) = -4.94SEFV(t-4) + 2.47RPR(t-8) + 18.15(t-1990) + 184.89, September 2011

PG(t) = -4.76SEFV(t-5) + 2.27RPR(t-9) + 18.29(t-1990) + 187.61, December 2011

where PG(t) is the monthly closing price (dividend and split adjusted) in U.S. dollars, t is calendar time.

In the lowermost panel of Figure 2, the predicted curve leads the observed price by 5 months with the residual error of $2.16 for the period between July 2003 and December 2011 (see Figure 3 for the model residuals). In other words, the price of a PG share is completely defined by the behaviour of these two CPI components.

The model does predict the share price in the past and foresaw a period of no growth in the fourth quarter of 2011. In January-March 2012, the price may fall.

Figure 1. Evolution of the price of SEFV and RPR.

Figure 2. Observed and predicted PG share prices as obtained using contemporaneous data

Figure 3. The model residual error.

Why history is not economics: Tell Me the Difference Between My Lai and Hiroshima

2012-01-20T13:20:00.000+01:00

A thoughtful essay by Brian Kaplan on EconLog: http://econlog.econlib.org/archives/2012/01/tell_me_the_dif.html

History as a soft science allows many compeletely opposite views at the same time. This is a problem of belief and self-defence. If economics as a science is just general judgement or belief then it should be treated as history, i.e. a biased view of prejudiced people with own interests.

Quarterly report on Boston Scientific

2012-01-20T12:08:00.000+01:00

We posted on Boston Scientific (BSX) in January, April, and October 2011 and presented a share price model for Boston Scientific as based on our stock pricing concept. Both models were similar and included the consumer price index of housing (H) and the index of durable goods (DUR). (Figure 1 depicts the overall evolution of the involved indices.) In the currently re-estimated model (i.e. through December 2011), the former defining CPI component led the share price by 6 month and the latter one by 4 months (slightly increased lags but contemporary CPIs are used in the December model).

Thus, here we update the original model using data through December 2011. The updated model has the same defining components and time lags with slightly different coefficients. Therefore, the original model provided a reliable prediction through the past year and further in the past. Currently, the best fit model predicts the share to fall below zero in the near future what is equivalent to bankruptcy. A similar prediction (negative share price) was obtained for Lehman Brothers and other financial institutions before they failed.

The best-fit 2-C models (March, September, and December 2011) for BSX(t) are as follows:

BSX(t) = -1.41H(t-5) – 2.85DUR(t-3) – 0.09(t-1990) + 630.92, March 2011

BSX(t) = -1.46H(t-5) – 2.71DUR(t-3) + 0.36(t-1990) + 615.69, September 2011

BSX(t) = -1.44H(t-6) – 2.21DUR(t-4) + 0.84(t-1990) + 546.90, December 2011

where BSX(t) is the (monthly closing adjusted for splits and dividends) share price in US dollars, t is calendar time. Both coefficients are negative, and thus the increasing consumer prices result in decreasing share price. The slope of time trend is almost negligible, i.e. $0.84 per year.

All models predicted the price at a three-to-four month horizon with standard deviation of $2.24 between July 2003 and December 2011 ($1.83 in March). The currently observed growth in the defining consumer price indices should drive the share price down. In the first quarter of 2012, the price may still drop below zero. However, the price will gradually recover above the zero line. The model residual in Figure 3 should return to the zero line somehow.

Figure 1. The evolution of H and DUR.

Figure 2. Observed and predicted BSX share prices. March, September, and December 2011 models. The price is negative by the end of 2011.

Figure 3. The model residual, i.e. the difference between the observed and predicted BSX share prices.

Tim Duy on Japan or why macroeconomics is wrong

2012-01-20T11:23:00.001+01:00

Tim Duy on Economists View posted (http://economistsview.typepad.com/economistsview/2012/01/fed-watch-japan-revisited.html) on the current recovery of Japan and also mentioned usuall macroeconomic rubbish on "lost decades". This is one of good examples showing that the mainstream macroeconomic theories are worthless and confusing. These people do not actually understand what drive a developed economy like the Japanese one.

We have already decribed the evolution of a developed economy as expressed by real GDP per capita, G. There are two component in play - inertial growth, A/G, and the change in a specific age population, dNs/Ns ( following paragraph is borrowd from our book "mechanomics. Economics as Classical Mechanics)

dG(t)/G(t)=A/G(t)+0.5dN_s(t)/N_s(t)dt (1.8)

where A is an empirically determined coefficient, N_s(t) is the number of people of the defining age. For Japan, the defining age of eighteen years has been found. Relationship (1.8) implies that the growth rate of GDP depends explicitly and entirely on the attained level of real GDP per capita and the population change. If to gather relevant terms on both sides of the equation, this relationship can be simplified in the following form:

d[G(t)-(At+C)]/G(t)= 0.5dN_s(t)/N_s(t) (1.9)

where C is the constant of integration, i.e. the initial condition of the initial value problem.

From (1.9) one can derive either the evolution of G or Ns depending on the purpose. Since the number of 18-year-olds can be estimated by integrating (actually by summation of discrete estimates) the left hand side of (1.9) with the measured annual values of G and also enumerated by population surveys one can compare results visually and statisticaly. Figure 1.23 (also from the book) demonstartes that the evolution of G follows up the evolution of Ns. Since G can not affect Ns the causality directio is opposite - the change in Ns drives G. From 1.23, one can understand that so called "lost decades" actually manifest the fall in the number of 18-year-olds since 1992. Accordingly, the years before 1991 are charaterized by increasing Ns and thus are called "economic miracle". Finally, one can extrapolate the younger age cohorts into the future and estimate the future evolution of G. Figure 1.23 shows that the years of low economic growth are left behind and the 2010s will be charaterized inertial growth only, A/G, since Ns will not be changing. This is not fast economic growth, A/G~ 1.5% per year, but is definitely better than the permanent depression of the 1990s and 2000s.

Figure 1.23. Enumerated and predicted number of 18-year-olds.

On the prostest of Harvard economics students

2012-01-20T10:33:00.001+01:00

Actually, it happened two months ago and I've just learnt it from Robert Johnson who posted on the failure of economic and financial theories in 2008 ( http://business.time.com/2012/01/19/economists-a-profession-at-sea/?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+timeblogs%2Fcurious_capitalist+%28TIME%3A+Business%29) . Obviously, there is a trade-off between theories and observations. Soft sciences, like history or philosophy, do not pretend to describe the world in quantitative terms. Hard sciences are fully quantitative and do not consider any deviation from the scientific method of proof, i.e. statistics and mathematics. Economics is somehow in-between the soft and hard sciences. Economists base their theories on assumptions from the blue sky but want the outcome to be considered as in the hard sciences. There is a clear and irresolvable conflict which surfaces over and over during large economic perturbations - one pretends to adequately describe by math and statistics what s/he pretends can not be adequately describe by math and statistics. Macro economists never want and always deny their theories to be tested econometrically, i.e. in scientific way. Hence, the current situation (i.e. the painful failure to predict large changes) with economics will be repeated over and over because it is embedded in the internal structure of the economic theories.

Students have always seen and complained on the intrinsic weakness on the macroeconomic postulates. Essentially, these postulates do not give any appropriate understanding of the real world and thus confuse. (In the end of Ec10 students are usually informed that nothing from they have just learnt actually works.) The protesting Harvard students just express this simple idea which has always been with them before but which they could not express openly in quiet times due to the concealed threat from their profs.

Our economic concept is based on observations and actually predicts quantitatively the evolution of developed economics. When these protests gather more supporters demanding scientifics knowledge our concept (fully following the method adopted in the hard sciences) will be more easily accepted as the only alternative to the "soft" economics.

Quarterly report on Aflac share price

2012-01-19T20:05:00.000+01:00

Here we routinely revisit the stock price model for Aflac Incorporated (AFL) which we obtained in October 2011 using our concept of share pricing. Accordingly, our goal is to test the original model and to update time lags and coefficients.

Last time we predicted the AFL price using the CPI estimates published by the BLS for September 2011. It was a preliminary model. The share price was defined by the consumer price index of household furnishing and operations (HFO) and that transportation services (TS). In September, the defining time lags are as follows: the HFO index led the share price by 0 months and the TS by 5 months. Adding new data for the period between September and December 2011, we have re-estimated the model and found some changes in the time lags: one and six months, respectively, as well as in the estimated coefficients. The relevant best-fit 2-C models for AFL(t) are as follows:

AFL(t) = -5.02HFO(t-2) – 2.87TS(t-6) + 20.42(t-1990) + 997.71, March 2011

AFL(t) = -4.63HFO(t-0) – 2.90TS(t-5) + 20.41(t-1990) + 953.49, September 2011

AFL(t) = -4.63HFO(t-1) – 2.87TS(t-6) + 20.23(t-1990) + 948.72, December 2011

where AFL(t) is the AFL share price in U.S. dollars, t is calendar time. The changes in time lags are shown in red.

In July 2011, we reported that the original model gave a correct prediction of the fall in Q2 2011. In September 2011, we showed that the contemporary fall in the price had to stop and expected a positive correction in Q4 2011. Figure 1 confirms our prediction and depicts the high and low monthly prices for an AFL share together with the predicted and measured monthly closing prices (adjusted for dividends and splits). The model residual error is depicted in Figure 2. The price will likely not change much in 2012Q1.

Figure 1. Observed and predicted AFL share prices.

Figure 2. The model residual error.

A share price for Advanced Micro Devices

2012-01-19T19:51:00.000+01:00

Here we address the stock price model for Advanced Micro Devices (AMD) first time. The model has been obtained using our concept of share pricing. Accordingly, our goal is to test the underlying concept and to estimate time lags and coefficients.

We have borrowed the time series of monthly closing prices of AMD from Yahoo.com and the relevant (seasonally not adjusted) CPI estimates through December 2011 are published by the BLS for March 2011. The evolution of AMD share price is defined by the consumer price index of rent of primary residence (RPR) and that hospital and related services (HOSP). The defining time lags are as follows: the RPR index leads the share price by 1 month and the HOSP by 4 months. The relevant best-fit 2-C models for AMD(t) is as follows:

AMD(t) = -2.21RPR(t-1) – 0.82HOSP(t-5) + 37.87(t-1990) + 267.01, December 2011

where AMD(t) is the AMD share price in U.S. dollars, t is calendar time. Figure 1 depicts the high and low monthly prices for an AMD share together with the predicted and measured monthly closing prices (adjusted for dividends and splits). As a rule, the predicted prices are well within the bounds of the share price uncertainty. The model residual error is shown in Figure 2. We will be reporting on AMD.

Figure 1. Observed and predicted AMD share prices.

Figure 2. The model residual error.

Alcoa Inc. share price through March 2012

2012-01-19T18:14:00.001+01:00

Three and nine months ago we reported on a share price model for Alcoa (AA), which is company from Materials subcategory of the S&P 500 list specialized in aluminum. Here we test and update the model using new data, including the monthly closing price in December 2011 and the estimated CPI components for December. The principal result is that the model has the same defining CPI components and time lags with slightly shifted coefficients. Therefore, the model is a reliable tool to predict the evolution of Alcoa at a three month horizon.

According to our general approach to share price modeling we decompose the observed time history of the monthly closing AA stock price (adjusted for splits and dividends) into a weighted sum of two CPI components, time trend and free term. Two defining CPI components are selected to minimize the model (RMS) error and may lead or lag behind the share.

The original and current AA model is defined by the (not seasonally adjusted) index of food away from home (SEFV) and the price index of rent of primary residence (RPR), as reported by the US BLS. The former CPI component leads the share price by 3 months and the latter is 5 months ahead of the share price. Figure 1 depicts the overall evolution of both involved indices through August 2011. It seems these indices have been evolving in sync since 2002 with the only step-like change in the SEFV index in 2008. We present three empirical models as estimated in April, October, and December 2011:

AA(t) = -6.71SEFV(t-2) + 3.34RPR(t-4) + 19.23(t-1990) + 298.87, Aril 2011

AA(t) = -6.61SEFV(t-2) + 3.22RPR(t-4) + 19.51(t-1990) + 300.89, October 2011

AA(t) = -6.47SEFV(t-3) + 3.08RPR(t-5) + 19.58(t-1990) + 302.45, December 2011

where AA(t) is a share price in US dollars, t is calendar time. Figure 2 illustrates the observed and predicted models for December 2011. The residual error is $3.05 ($3.04 in October and $3.12 in April) for the period between July 2003 and December 2011. Figure 2 also shows monthly high and low prices as the uncertainty in the monthly closing price as the best share price estimate. Since the closing price has to characterise the whole month by one value the high and low prices might serve as statistical bounds.

One can expect the share price to rise through March 2012.

Figure 1. Evolution of the price of SEVF and RPR.

Figure 2. Observed and predicted AA share prices.

It is better to see one time than to hear 100 times

2012-01-19T12:09:00.000+01:00

I have mentioned that the graph shown by Lane Kenworthy

has a significant problem with data. Here is a graph from my paper from 2005 on average (and median) personal income in the USA.

Fig. 3. Ratio of the total number of people and the number of people with income in various age groups. The youngest group is characterized by a participation factor of 0.75. During the late 1970s, participation factor in other age groups increased from 0.82-0.85 to 0.92 -0.99.

One can see that personal income definition was dramatically revised around 1977 and the portion of people with income jumped by 10% to 15%. The values above 1.0 also manifest the absolutely stupid difference between people counted by the BLS and the Census Bureau. There are to United States according to their statistics. The BLS does not take into account the closure error after decennial censuses and thus uses wrong populations, as I mentioned many times in this blog.

All in all, it is very suspicious that the deviation between GDP per capita and median income started the very same time.

Blog as a precursor of academic articles

2012-01-19T11:39:00.000+01:00

Joshua Gans on Digitopoly extends the discussion of open science and academic publications. He shows an example when his quick reaction in blog has resulted in a formal academic paper. This is a good illustration that ideas play superior role in academic research and data retrieving and processing becomes a routine.

There are couple more examples one can find in this blog (and its predecessor – Inflation in the USA). The first case is related to labor force dependence on real GDP. I have a series of post during several months which reported the evolution of this idea from zero to a complete academic paper published in the Journal of Applied Economic Sciences (ranked in Scopus and RePEc). Briefly, we have shown that the rate of participation in labor force depends (Rsq ~0.8 to 0.9) only on real GDP per capita, G, in developed countries. Standard econometric tools demonstrated the presence of cointegration between these two variables. As a benefit, we found that labor force productivity is also a unique function of G. Since 2006, all models were validated by new data. Each turn to the final model and its validation was carefully documented. All new results have been firstly posted in the blog.

Lately (inspired by Paul Krugman), we found a new way to represent famous Okun’s law (unemployment rate vs. real GDP) and its counterpart – the link between the rate of employment and real GDP per capita. We meticulously reported all quantitative results before published them in two working papers [1,2]. The latter is ready for submission to a peer-reviewed journal but some new findings need to be includes. Specifically, we found and reported in this blog that the GDP deflator in developed countries has been spoiled by definitional revisions. Quantitatively, this means that one has an artificial break in the relevant time series for real GDP. Not surprisingly, the estimated Okun’s law versions for developed countries have artificial “structural” breaks in the very same years. Instructively, these breaks in the estimated Okun’s laws and in the links between real GDP and the rate of employment for developed economies had been obtained before (June 2011)we found the breaks in the GDP time series (October 2011). This may serve as a strong justification of our models for unemployment and employment. This part was reported in this blog and needs to be carefully described in the already prepared papers. Funny, but Lane Kenworthy did not dig into this artificial break problem when represented his graph of increasing income inequality. One should be very carefull with data and blogosphere can help much. One needs just to formulate a proper request.

All in all, this blog was launched in order to report on important phases of our scientific research. After several years, I am sure that it fulfills its purpose and demonstrates the importance of quick reaction to new ideas caught in the blogosphere.

Will BofA shares fall to $1?

2012-01-18T13:41:00.001+01:00

Two months ago we presented a share price model for Bank of America (BAC) and predicted the share falling below zero (a step to bankruptcy). The model has a history of three years and is based on the decomposition of the price into a weighted sum of two consumer price components, a linear time trend and a constant. All coefficients, time lags and CPIs were estimated by the least squares as applied to the model error. Here we revisit the model using new data through December 2011. The newly estimated model differs from the preliminary one due to strict constraints on time lags and new data. The best-fit two component model for BAC(t) is as follows:

BAC(t)= -2.30OHF(t) + 1.12H(t) + 2.17(t-1990) + 167.83

where OHF is the consumer price index of other food at home and H(t) is the index of housing, both having notime lag behind the price (see Figure 1 for the evolution of the CPIs). Both CPIs are available only for November 2011, and thus we actually have a one month lead of the CPIs, if they would have been already available for December 2011 (will be available on January 19, 2012). One has to reestimate the model when the CPIs for September are published by the BLS . The standard deviation of the modeling error is $2.90 for the period between July 2003 and December 2011. Figure 2 depicts the observed and predicted prices and indicates that the market overestimates the share above the predicted level. The overestimation is also clear from Figure 3 where the model residual is displayed.

Despite a small increase in the predicted price in December, we still suggest that Bank of America might have problems, which could bring its share price down to $1 in the near future. Similar problems had Lehman Brothers and other banks several months before bankruptcy.

Figure 1. The evolution of the defining consumer price indices.

Figure 2. Observed and predicted BAC share prices.

Figure 3. The model residual.

Krugman on increasing inequality

2012-01-18T11:12:00.000+01:00

Paul Krugman posted on increasing economic inequality borrowing a graph from Lane Kenworthy. The graph shows a rapid deviation between the median family income (as published by the Census Bureau) and real GDP per capita (supposedly reported by the BEA) since 1977/1978.

There are two problems with this graph and post. Firstly, one should not ignore the fact that the GDP deflator and CPI started to rapidly (20% per year) deviate since 1977/1978, i.e. exactly the time of the deviation between median family income and real GDP per capita in the presented graph. Lately, we have reported the descrepancy between the CPI and GDP deflator and the problem with real GDP calculations: http://mechonomic.blogspot.com/2011/10/real-real-gdp.html

Secondly, one should not also ignore that fact that the portion of people with income, i.e. those who counted in the statistics of personal and family income, jumped from ~0.82 to ~0.95 after the 1977 revision to the procedure of personal (and thus family) income surveys (CPS ASEC). We have described and assessed this fact in our paper on the evolution of average and median income (http://ideas.repec.org/p/inq/inqwps/ecineq2005-11.html) . This step in the income time series severely disturbed all income statistics.

This is not to say that there is no reason for the deviation between median family income and GDP per capita, but there are at least two artificialities (breaks) in the corresponing time series. One should not interprete this deviation as a meaningful one before considering the influence of artificial breaks.It might happen that the deviation just manifests the change in measurements.

Krugman on open science and economics

2012-01-18T10:36:00.000+01:00

Paul Krugman posted an excellent piece revealing major tendencies in data/idea exchange via the Internet. With a smart request, one can retrieve what s/he needs right now (appropriately formatted) skipping all unnecessary information. This option makes almost all research processes much faster except the one related to the creation of new ideas. Thus, this is the time for ideas to compete.
With all (free) math and statistical tools it takes couple days now to process huge amounts of data and get result in publishable formats, i.e. nicely drawn figures and tables. Why wait years for a journal publication? However, all references in any publication still have to be “good”, i.e. top ten journals in the field are required independent on the quality and priority of ideas in the referenced publications. This is important for career and general prosperity. This is like wisdom of old days. Elder generations always had life wisdom advantage to control youngsters. It’s not working any more with technology revolutions every 10 years and signers/football players/computer geeks earning millions.
Can we expect the end of influence of major journals in the economic profession? And what is the difference between physics and economics? The latter question is easier to answer. Physics is chiefly based on experiments which can be formally validated. The scientific community follow up common rules of testing hypothesis, procedures and results and thus represents one big expert. In that sense the influence of top journals as subsets of the unified expert will never fade away. In economics, there is no harmonized understanding of underlying economic processes and there is no formal methodology to reject or accept any hypotheses and/or results. It makes journals to be “political” and follow only one “school of thought” (For example, no American macroeconomic journals like the European school of econometrics demanding statistical justification of macromodels.) . In that sense, the influence of top economic journals will fade away when one formulates a scientific methodology to reject or accept any hypotheses and/or results”. Currently, these journals are strongholds of thinking tanks.
The "Internet economics” is the platform for revolutionary findings in economics since it is not limited by artificial rules of top journals. It’ll take some time to understand that there is a new paradigm. But this is inevitable if the new paradigm describes reality better than any of the existing economic theories. The scientific community easily abandon any theory which is proved to be wrong.

Bourgeois revolution in Russia

2012-01-15T14:41:00.001+01:00

All current problems and issues will be solved gradually, or revolutionary, as a standard bourgeois revolution begins. As always, there is the question of property protection. Much of the current private property, which has been obtained and is still distributed by clearly illegitimate means, is under "krysha" (semi-criminal protection) of the whole system of government from presidential power to the courts. This practically illegal (albeit large) pieces of property (mostly related to various forms of corruption) needs the state for illegal protection, which naturally includes the elected parliament. Words about the defense of the motherland and the unity of Russia can be understood as a signal "not surrender its"

But there are more and more people with the legally acquired property from good education and an expensive apartment to honest business. Their rights are not protected effectively by the current executive power, legislature and judiciary. There is only one legitimate way to change the system for the honest owners - to become the chosen representative, as the executive and judicial parts just co-opt people on the basis of the commitment to the illegal property. The 2011 electoral fraud has specifically affected these legal owners and instilled in them the deepest anxiety for the future and anger at almost all the authorities. I think the actual number of people supporting demonstrations is counted in millions. (A good education is also a valuable property when the job market does not work properly.) It should be noted that the opposition is of no interests for the owners as well as the power itself.

All we want to achieve is the essence of the transition to the right protection of property. The 2011 Duma elections have shown that the power does not accept and will actively oppose any change. I do not think that the massive protest was just an emotional reaction to the fraud. It was the actual understanding that the current power neglects our basic rights as citizens and property owners (in the broadest sense). Hence the protest will be a long-term one. The most fundamental problem is not resolved. The protest is expressed with the introduction of elements of revolution. We have to solve the main issue, constituting the essence of everyone’s interests - the protection of all kind of legal property, including the intellectual one. Therefore, the opposition will only grow in number and get more and more implacable as the price of the property grows. The protesters are mainly young people who owe nothing to the state as such. All they have is the result of their own efforts. In contrast to the older generation they do not understand why they have to endure oppression of power. It gave them nothing.

Thus, we have standard problems resolved by bourgeois revolutions.

New service for researchers: Academia.edu

2012-01-03T11:52:00.001+01:00

I've joined Academia.edu. It really helps to increase the visibility of my papers in geophysics and economics. I definitely recommend it.

Academia.edu is a platform for academics to share research papers. The company's mission is to accelerate the world's research. Academics use Academia.edu to share their research, monitor deep analytics around the impact of their research, and track the research of academics they follow.

Perspectives of cross correlation in seismic monitoring at the International Data Centre

2011-12-21T12:01:00.000+01:00

This is a preprint of our paper submitted to the Pure and Applied Geophysics:

Abstract

We demonstrate that several techniques based on cross correlation are able to significantly reduce the detection threshold of seismic sources worldwide and to improve the reliability of IDC arrivals by a more accurate estimation of their defining parameters. More than ninety per cent of smaller REB events can be built in automatic processing while completely fitting the REB event definition criteria. The rate of false alarms, as compared to the events rejected from the SEL3 in the current interactive processing, has also been dramatically reduced by several powerful filters. The principal filter is the difference of arrival times between the master events and newly built events at three or more primary stations, which should lie in a narrow range of a few seconds. Two effective pre-filters are f-k analysis and Fprob based on correlation traces instead of original waveforms. As a result, cross correlation may reduce the overall workload related to IDC interactive analysis and provide a precise tool for quality check for both arrivals and events. Some major improvements in automatic and interactive processing achieved by cross correlation are illustrated by an aftershock sequence of a large continental earthquake. Exploring this sequence, we describe schematically the next steps for the development of a processing pipeline parallel to the existing IDC one in order to improve the quality of the REB together with the reduction of the magnitude threshold. The current IDC processing pipeline should be focused on the events in areas without historical seismicity which are not properly covered by REB events.

How can we validate the statistics of the Russian elections?

2011-12-14T21:00:00.000+01:00

Statistically, there is a relatively simple way to validate (or demonstrate falsification) the overall distribution. It is commonly used by census bureaus and statistical agencies. One takes a random and small subset from a studied distribution (statistics) and repeat survey (voting) under full control. If the re-estimated statistics is quite different from the previously obtained result one may express a doubt in the accuracy of the original survey (voting). The size of the re-estimated set should be defined by the originally observed statistics and specifically from the polling stations with most suspicious results.

Therefore, one does not need to repeat the full election process everywhere. We need only a relatively small number of polling stations which can be determined by lottery from the set with distributions of votes in favour of United Russia far from the expected Gaussian distribution. United Russia would be happy to prove that these results are not falsified to calm down the protests and indignation.

20,000,000, anyway

2011-12-13T21:37:00.002+01:00

In our previous post, we have approximated the dependence of the number of polling stations on the portion of United Russia at a given polling station. Now we can estimate the number of votes falsified by UR from simple functional dependences obtained in the post, i.e. the expected normal distribution and the sine function after 29%.

Let’s take one polling station with, say, 60% in favor of UR. The mean value estimated from the normal distribution is 29%. Therefore, the expected value is 29% as well and the portion of likely falsified votes in 31%. Here we disregard the falsification technique. It can be redistribution of votes from other parties or direct adding of faked votes to the correct result. In any case, 31% of the total number is wrongly assigned to UR. For a middle size polling station of, say, 1000 people it makes 310 wrong votes. Our estimate of the number of polling stations between 60% and 61% is 1160. (Notice that the original bins were only 0.5%-wide.) Hence, there are 1160x310=360000 wrong votes altogether. In the range between 29% and 30%, the difference between the normal and sine distribution are small and the number of wrong votes is only 600. In the range between 99% and 100%, one has 472000 votes hacked.

Summing all bins up one obtains 20,000,000 votes biasing the elections outcome. Same as estimated initially using very crude approximations.

The SINE function of the Russian elections

2011-12-12T22:59:00.005+01:00

Several days ago we presented a preliminary analysis of the distribution of votes on polling stations in Russia. We borrowed the original graph from Maxim Pshenichnikov and crudely estimated the difference between the observed curve and that expected from normal distribution (Gaussian) of parties portions. All parties except United Russia showed the expected normal distribution, which is also observed during elections in other countries, where UR does not rule. Today we illustrate the difference and demonstrate quantitatively the difference. Moreover, we propose a simple functional dependence for those who want to carry our own research. Figure 1 reproduces the original distribution (brown curve), which depicts the number of polling stations in 0.5% bins as a function of the UR portion of votes - from 0% to 100%. we have approximated the original distribution with a normal one with mean=29% and stdev=5%. Black curve fits the left wing of the measured function and fails on the right one. When elections are honest, the distribution must be normal as the black line shows. However, the observed brown line is better approximated by a sine function shown by red line. We have found the following equation (not optimal):

s(V) = 637+403sin(V/18)

where V is the portion of the UR votes (x-axis), s(V) is the number of polling stations in a given V-bin. The curve starts from 30%, where the normal distribution fails to fit the observed line. All in all, the elections’ results demonstrate a sine line of the UR success.

having this function one can easily estimate the number of polling stations with falcified elections and the number of voices falcified in favor of UR.

Figure 1. The number of polling stations in 0.5% bins (vertical axis) with a given portion of votes (between 0% and 100%) for the UR.

Can statistics prove the (Russian) elections fraud?

2011-12-11T10:40:00.002+01:00

Many people think that statistics can no prove the Russian elections' fraud. This is not the case, however. Statistics allows revealing reliable links between various measurable parameters during the elections which can not exist at all. For expample, the results of elections (http://www.vedomosti.ru/tnews/geo/moscow-elections )

in psychiatric clinics in Moscow show that the United Russia had more than 90% of votes, including legally capable patients. This is a fantastic result for the UR but very bad message for its supporters. The large percentage in favor of the UR evidences in statistical sense that there is a reliable link between voting for the UR and the predisposition to a psychiatric disease. A medical doctor has to include this link in the differential diagnosis of the diseases. When a patient comes, the doctor first has to ask which party s/he has voted for. And this link exists only in Russia.

I am not sure that the UR would agree that there was no fraud in this specific area which would characterize the party proponents as subject to psychiatric diseases. There are many other links that look absolutely weird and the UR should not be happy with them.

P.S. Not serious.

As a novice in psychiatry I missed an important opportunity – the propensity to the UR is contagious and can easily be transmitted in closed groups. Since this propensity has strong correlation with the predisposition to psychiatric disease the Disease Control and Prevention Centers should issue an alarm. Before that individual measures should be taken – gloves and mask when communicate with the UR people.

A thought

2011-12-10T09:47:00.001+01:00

One is not able to buy for money what s/he must sacrifice to get rich.

Russian elections:statistical bias between 10,000,000 and 20,000,000 votes

2011-12-08T18:32:00.000+01:00

How many voices might be stolen in Russia?

Statistics is a powerful tool to reveal frauds. Almost everything in this world is distributed according to a few simple laws: normal, power law, exponential, linear. The most famous is the normal distribution which happens often because of the Central Limit Theorem. Briefly, if some process is controlled by a very big number of parameters its outcome usually obey the normal distribution or Gaussian. The 2011 elections in Russia is a good example of the Gaussian distribution and potential fraud. Figure 1 displays the distribution of the number of polling stations as a function of the percentage given to five participation parties (borrowed from http://oude-rus.livejournal.com/542295.html ) : EP - United Russia, КПРФ – communist party, etc.). The comminists’ curve demonstrates an example of an approximately normal distribution between 3% and 40%. There is a bias at low values which we can ignore for this case as a consequence of the United Russia behaviour. Specifically, the EP curve demonstrates a clear Gaussian between 15% and 27% and then a long heavy tail. Here we have to notice that it is the only party with this tail. Moreover, there are weird peaks around “magic” numbers: 50%, 55%, …,100%. Hence, the expected normal behaviour is violated and it can be only the result from a voluntary action. If the number of causes behind voting is large then only normal distribution can be observed. If such a distribution would be demonstrated by some lottery, it would have been a fraud and the distribution could be used as a proof. (It is worth noting that heavy tail distributions are observed in stock market, where they are less prominent.)

Using the EP distribution one can easily estimate the number of votes above that dictated by normal distribution. Let us continue symmetrically the EP curve beyond 27% - a mirror reflection. The distribution should fall to zero at approximately 50% (x-axis). The difference between the measured EP curve and the imaginary extension is the bias introduced by a control force. On average, there are 400 polling stations in any 0.5% bin after 50% in the EP curve. One has 400x2x50=40,000 polling station with biases.

Now we have to evaluate the number of votes. The mean value for the EP distribution is 27%. Then for all polling stations the bias can be estimated as the measured value less 27%. Between 50% and 100% one has 48% on average, i.e. (100%+50%)/2 – 27%. For an average polling station with 1000 voters one has a positive bias of 480 voters. For all 40,000 stations this gives 19,000,000. This figure depends on the average size of polling stations. For 500 voters stations the final number is 9,500,000.

Anyway, it is big.

Figure 1. The number of polling stations in 0.5% bins (vertical axis) with a given portion of votes (between 0% and 100%) for five major parties.

On faulty philosophy of economics

2011-11-13T14:05:00.000+01:00

There is a vivid discussion of the 2008/2009 economic and financial crisis which often touches upon the failure of the mainstream economic theory to predict and describe major events. This theory is actually a bunch of assumptions, sometimes mutually exclusive. There are common features, however, which are shared by all schools of economic thought. They do assume that economic agents (individuals or firms) have some freedom and can act according to their own rational or irrational choices. When these choices are not well coordinated, synchronized and balanced throughout a given economy slowdowns and even recessions are likely to happen. In economic literature, these events are often introduced as shocks (to demand or supply). The term “shock” is a euphemism of the physical realization of unknown psychological processes in the economy. Generally, the mainstream economics does not try to explain these shocks as a result of real processes.

We see an inherent problem in the conventional approach to economic processes. It skips the first and fundamental step which is a must for any theory. The basic assumption should be that economic agents do not have a free will and follow up only prescribed trajectories. This notion is supported by the observation of a “frozen” personal income distribution. The normalized income distribution is constant over time as obtained from the reports of Annual social and Economic Supplements to the Current Population Surveys conducted by the Bureau of Census.

All in all, we assume that no economic agents can change the rate of real economic growth as expressed by real GDP per capita from inside the economy. There are no endogenous forces which can divert the economy from its predefined trajectory of inertial growth. (We do not consider here wars, pandemics and any economy-wide catastrophes.) This system is also resilient to exogenous forces because the agents can react only the predefined way to any events. Then, the only driving force is the quantitative change in the distribution of agents. That’s why the influx of individuals is the most probable source of shocks to the economy. The outflow is stationary and cannot change the system. As a result, the evolution of real GDP per capita is driven by the number of young people entering the economy. And this is our fundamental model, which must be the basis for any model with mobile agents. For no clear reason, this model is rejected by the conventional economics.

In classical mechanics, the most fundamental laws and models are first based on the assumption that all objects are identical and have ideal properties. In physics textbooks, one operates with points, ideal spheres, perfect rigidity, constant coefficients, and so on. In reality, there are tangible deviations from the perfect world of classical mechanics, but all fundamental laws are always valid. Moreover, before one starts to inspect the real world s/he has to learn classical mechanics with its perfect relationships. The mainstream economics has made a trivial philosophical mistake and missed the most fundamental part of scientific approach. We have filled this gap with our book “mecħanomics. Economics as Classical Mechanics. ” It resolves the most urgent problems of economics as a science and provides a solid basis for the further development. We use only quantities of economic agents whose only property is to exist. In that sense they are similar to ideal rigid spheres and do not have freedom.

Some science instead of economics

2011-10-29T09:00:00.002+02:00

I am an amateur economist and economics is rather entertainment for me. My profession is geophysics and seismology. We have started an important study and here is an extended abstract (https://na22.nnsa.doe.gov/prod/researchreview/2011/PAPERS/06-01.PDF pdf) presented in September 2011.

ANALYSIS OF THE 2008 CHINESE EARTHQUAKE AFTERSHOCKS USING CROSS-CORRELATION

Abstract

Our objective is to assess the performance of a cross-correlation technique as applied to automatic and interactive processing of aftershock sequences at the International Data Centre (IDC). This technique allows a flexible approach to time windows, frequency bands, correlation thresholds and other parameters controlling the flux of detections. For array stations, we used vertical channels to calculate a unique cross-correlation coefficient. All detections obtained by cross-correlation were then used to build events according to IDC definitions. To investigate the influence of all defining parameters on the final bulletin, we selected the aftershock sequence of the March 20, 2008, earthquake in China with mb(IDC) = 5.41. As templates, fragments of P- and Pn-waves from two sets of the IDC
Reviewed Event Bulletin (REB) events were selected: all 19 events from the second and third hour after the main shock and 50 events from the entire sequence with mb between 3.0 and 4.0. By varying the threshold of correlation coefficient and F-statistics which were applied to original waveforms and to the cross-correlation time series, we obtained several bulletins with different numbers of events, which could be compared to the original REB and also checked manually. These events were split into four categories: (1) new events having a counterpart (origin time within 10 sec) in the REB, (2) new valid events not having a counterpart in the REB, (3) valid REB events not having counterparts in any bulletin created by cross-correlation, and (4) bogus (invalid) events created by crosscorrelation
and which are in the REB.

Real real GDP

2011-10-26T13:52:00.003+02:00

We have already reported that real GDP in the United States is biased by the change in definition of the GDP deflator around 1978. (According to “Concepts and Methods of the U.S. NIPA” the growth rate of real GDP is the growth rate of nominal GDP reduced by the overall change in prices as expressed by the GDP deflator or the economy-wide price index.) Figure 1 shows that before 1978 the GDP deflator and CPI were similar and their difference is negligible since 1929. In 1978, a new definition of the GDP deflator was introduced and the curves coinciding before 1978 started to deviate. In 2010, the deviation was approximately 20%.

A reasonable assumption on the new definition of the GDP deflator is that it should also be applied to the time series before 1978. This would reduce the bias introduced in the time series around 1978. Figure 1 demonstrates (dashed line) that the growth rate of the CPI after 1978 is approximately 20% higher that the rate of the GDP deflator growth. Without loss of generality, one may assume that the GDP deflator had been growing at a rate approximately 20% lower than the CPI before 1978. In Figure 1, green line represents the GDP deflator before 1978. Since the growth rate of the GDP deflator was lower the over all change between 1929 and 2010 is also smaller than that of the CPI.

The difference between the GDP deflator and CPI has an immediate consequence as related to real GDP. When applied to the real GDP estimates published by the Bureau of Economic Analysis, the corrected GDP deflator provides a more accurate time series. One must use this corrected time series in economics and econometric research in order to avoid the apparent bias.

To begin with, we have updated our comparison of real GDP and real GDP per capita growth. This comparison has demonstrated that the fall in real GDP (recession) has actually returned the growth trajectory to the long-term trend and there is no output gap as estimated from real GDP time series. Figure 2 depicts two old and two new (corrected) curves. One can see that the new curves are above the old ones since the corrected GDP deflator is lower than the CPI and the updated real GDP estimates are higher than the original estimates. The corrected real GDP curve implies a much larger output gap that makes this hypothesis truly void. Essentially, the growth rate between 1930 and 1960 was so high that it can never be repeated. As a result, the Solow model (constant returns to scale) behind the output gap is likely to be wrong.

Figure 1. Cumulative growth rate (the sum of annual inflation rates) of various definitions of inflation since 1929.

Figure 2. Old and corrected (corr.) estimates of real GDP and real GDP per capita. The former estimates are below the new ones.

Update 28.10.2011. Corrected table of real GDP per capita and real GDP in 2005 US $

2010 42270 13108145194

2009 41377 12722814211

2008 43242 13181625195

2007 43791 13225891467

2006 43399 12978410715

2005 42681 12643309646

2004 41792 12265967955

2003 40769 11857542216

2002 40108 11556298822

2001 39769 11347579296

2000 39750 11226180266

1999 38592 10779826176

1998 37238 10283422652

1997 36102 9854329716

1996 34977 9433786578

1995 34112 9093849856

1994 33671 8870793305

1993 32747 8523454654

1992 32255 8287019110

1991 31614 8015097420

1990 32112 8033812272

1989 31877 7885955399

1988 31069 7613800209

1987 30115 7313216945

1986 29443 7086429569

1985 28717 6849176802

1984 27823 6577190262

1983 26186 6136243938

1982 25282 5870935476

1981 26030 5987108240

1980 25640 5838894640

1979 26010 5855007060

1978 25503 5677707387

1977 24150 5320037949

1976 23103 5038386795

1975 21814 4711459533

1974 21689 4639185468

1973 21796 4619427179

1972 20696 4344618556

1971 19729 4097469291

1970 19161 3929633433

1969 19185 3889478681

1968 18673 3748418138

1967 17905 3558699243

1966 17580 3456136296

1965 16656 3237016460

1964 15817 3035727724

1963 15129 2864008171

1962 14684 2739924324

1961 14039 2579516585

1960 13910 2514340896

1959 13838 2451111168

1958 13078 2277479719

1957 13353 2287102029

1956 13298 2237003680

1955 13280 2194815712

1954 12594 2045223652

1953 12885 2055915430

1952 12487 1959856824

1951 12096 1866190324

1950 11400 1729138416

1949 10677 1592940944

1948 10795 1582831140

1947 10309 1485823522

1946 10482 1482021159

1945 11855 1658907921

1944 12093 1673619102

1943 11231 1535740268

1942 9643 1300405392

1941 8175 1090577563

1940 7044 930616096

1939 6542 857231031

1938 6120 795393311

1937 6356 819695449

1936 6072 778270922

1935 5389 686317930

1934 4963 627750020

1933 4535 570028178

1932 4683 585161480

1931 5488 681318834

1930 5933 730845272

1929 6562 799800744

Real GDP is not correct. Cntd

2011-10-22T17:39:00.000+02:00

I have found some more data on the GDP deflator and CPI. These are time seried for France and Japan.
For France, we found a structural break in Okun's law around 1993. For Japan, it was in 1975.
Figures blows validate our finding that both structural breaks were artificial and induced by the change in real GDP definition.

Real GDP is NOT correct

2011-10-22T14:09:00.000+02:00

This is an extension of the story on wrong metrology of macroeconomic measurements. Economics chiefly fails and produces a great amount of counterproductive work due to wrong measurements of basic macroeconomics variables. In our previous post we focused on real GDP in the US and here we extend the case by Australia, Canada, and the United Kingdom. As mentioned before, we have devoted enough efforts to reveal and recover many trivial cases in our book “mecħanomics. Economic as Classical Mechanics”.

Real GDP (see Concepts and Methods of the U.S. NIPA for details) is the difference between nominal GDP and the GDP deflator (price index). The latter is not easy to calculate or even evaluate. In this post, we found that it is so much a sophisticated problem that before 1978 there was no practical difference between the cumulative inflation values of the CPI and the GDP deflator in the US, as Figure 1 demonstrates. (The cumulative inflation, i.e. the cumulative sum of inflation rates, is different from price index when differently calibrated in the beginning.) Effectively, the curves in the Figure diverge from 1978. There is no direct statement about the reasons of the change in definitions in the aforementioned conceptual document, but we might guess that this is likely related to the introduction of a new methodology to evaluate the overall price inflation. This difference has affected our analysis of Okun’s law and forced the introduction of a structural break in 1978 in the dependence between unemployment (and employment) rate and the rate of real economic growth. As we lately reported, this was an artificial break completely related to the change in real GDP definition in 1978.

Thus, before 1978 the CPI was used to estimate of the overall price inflation. Since 1978, the GDP deflator has been used. The difference between these two variables can not be neglected: the cumulative change in inflation between 1978 and 2009 is 20 percentage points. This implies that when applied to the estimates before 1978, the concept of the dGDP would result in a bigger change in real GDP estimates. The overall real GDP increase since 1929 should be much larger in the current definition of the GDP deflator is applied.

To validate this finding we have borrowed data from the OECD and calculated the CPI and dGDP cumulative inflation in Australia, Canada and the UK, as shown in Figure 2. There are clear breaks in different years: for Australia in 1983 (also 1983 was estimated from a structural break in Okun’s law); for Canada – 1980 (1982 was estimated from a structural break in Okun’s law); for the UK – 1979 (1982 was estimated from a structural break in Okun’s law). For the UK, the CPI and dGDP curves start to diverge in 1979 but the pace of deviation is very slow and the year of structural break in Okun’s law is hard to determine accurately.

One can conclude that all structural breaks in the previously estimated models of the rate of unemployment (Okun’s law) and the employment/population ratio for the US, Australia, Canada and the United Kingdom were entirely artificial and forced by the change in real GDP definition in the years of these breaks. (The OECD does not provide sufficient data length for other modeled countries and we need to find other sources of information for France, Japan and Spain.) Hence, real GPD estimates are incompatible over the break years and thus wrong. One must not use them for modeling and statistical analysis.

Real GDP is NOT correct!

Figure 1. Cumulative rate (the sum of annual inflation rates, what is different from inflation index) of inflation in the United States since 1929, as described by the CPI and dGDP.

Figure 2. Same as in Figure 1 for Australia, Canada and the United Kingdom.

... as a young economist

2011-10-22T11:31:00.001+02:00

RePEc (Research Papers in Economics) is the largest archive of economic papers (articles/chapters/software) with ~30,000 voluntary participants from 75 countries. It provides a number of rankings including that for young economists. I am not a young scientist but started my amateur research in economics in 2003 with the first paper published in 2005. Threfore, I belong to the category of young (10 years of less) economists with rank 86 (http://ideas.repec.org/top/top.young.html) . It is also the second rank for those started after 2005. Not bad to begin with.

Weird PPI of oil - illustration

2011-10-20T18:26:00.003+02:00

Following our previous post on oil price we compare the price of oil futures (dark blue) and PPI of oil (BLS estimates) between 2007 and September 2011. Even simple visual inspection shows that the September's PPI estimate differs from its expected value when converted from oil price. Why?

Update 20.10.2011

I have replaced the Figure with not seasonally adjusted PPI and medium mon thly oil price between 2008 and September 2011. Now the difference in September is prominent.

Do not understand the growth in the producer price index of oil

2011-10-19T16:18:00.001+02:00

It looks weird. Figure below shows daily change in oil price futures during the previous three months. There is no big difference between August and September 2011. I would estimate the change as negligible. At the same time, the PPI of crude petroleum (not seasonally adjusted) grew from 241 to 275.9, which is approximately the level of June. It should be a mistake.

... and a bit more models

2011-10-16T18:55:00.001+02:00

More models

2011-10-16T17:20:00.000+02:00

Share price models for selected S&P 500 companies

2011-10-16T16:58:00.000+02:00

Avon Products share price model

2011-10-12T20:47:00.001+02:00

Here we present a brand new share price model for Avon Products (NYSE: AVP). In April 2011, this model also showed a higher level of reliability and described the price through March 2011. We intentionally skipped it to publish in this blog because the predicted price showed just a small fall. The model was based on our concept linking share pieces and consumer price indices. The share price model for Avon Products was defined by the index of other household equipment and furnishing (OHEF) and that of public transportations (TPU). Figure 1 illustrates the evolution of these indices. In the April model, the former CPI component led the share price by 8 months and the latter one led by 5 months.

Here we revisit the model using the monthly closing prices (adjusted for splits and dividends) and CPIs for the period through September 2011. (The CPIs are available only for August 2011.) The principal result is that the underlying model is practically the same as six months ago with the same time lags but slightly different coefficients. This model predicted the fall in the price observed since June 2011 five months in advance. The March and September models for AVP(t) are as follows:

AVP(t) = -2.43OHEF(t-8) – 0.33TPU(t-5) - 5.22(t-1990) + 392.49, March 2011

AVP(t) = -2.25OHEF(t-8) – 0.33TPU(t-5) - 4.58(t-1990) + 366.41, September 2011

where AVP(t) is a share price in US dolalrs, t is calendar time. Relevant coefficients are both negative. The slope of time trend is also negative. There is some fluctuation in all coefficients caused by the uncertainty in measurements of both the stock prices and CPIs. Nevertheless, both models provide an accurate prediction at a five-month horizon as Figure 2 depicts.

Currently, the model shows that the price has reached a stable level of $20 per share and will not be changing in the fourth quarter of 2011. The predicted curve in Figure 2 (both versions are depicted) leads the observed price by 5 months with the residual error of $2.90 ($3.08 in April) for the period between July 2003 and September 2011. The model residual for the same period is shown in Figure 3.

Figure 1. Evolution of the price of OHEF and TPU.

Figure 2. Observed and predicted AVP share prices. Upper panel – March 2011; lower panel – September 2011.

Figure 3. Residual error of the model.

Beware of the BEA!

2011-10-12T12:52:00.002+02:00

In our previous post, we mentioned a structural break in the Okun’s law around 1978. We explained this break by an artificial change in the definition of the GDP deflator (as defined by the Bureau of Economic Analysis) in the very same time, as also was described in this blog. Here we test quantitatively the change in the estimated Okun’s law coefficients as related to the introduction some new definition of GDP.

Figure 1 displays two time series – the consumer price index, CPI, and the gross domestic purchases price index, dGDP. These time series diverge since 1978 and thus the estimates of real GDP, which is nominal GDP reduced by the GDP deflator, are biased relative to the period before 1978. One should not use the real GDP time series as it is published by the BEA when modelling longer time series including years before and after 1978.

The CPI and dGDP curves coincide when the latter one is corrected by a factor of 1.2, as is also shown in Figure 1. Therefore, one must use the corrected GDP deflator when modelling a time series covering periods before and after 1978.

Figure 1. CPI, dGDP, and 1.2*dGDP.

We made a silly mistake in our study of Okun’s law when tried to use the estimates of real GDP published by the BEA and had to introduce an artificial structural break in order to fit both periods. The best-fit model (Okun’s law) minimizing the RMS error was as follows:

du = -0.406dlnG + 1.113, t<1979

du = -0.465dlnG + 0.866, t>1978

where du is the change in the rate of unemployment and dlnG in the change rate of real GDP per capita. All coefficients we estimated by a LSQ technique minimizing the overall model error. Figure 2 shows the predicted and observed rate of unemployment between 1950 and 2010.

Thus, the structural break expresses itself in a change of slope and intercept around 1978, this year was also estimated in the same LSQ procedure. The ratio of slopes is 1.15, i.e. very close to 1.2 obtained from the CPI and dGDP. Considering the uncertainty in both slopes in the above relationship, which is approximately 0.05, one can conclude that the break in 1978 is entirely artificial and our version of Okun’s law has no structural breaks whatsoever. Essentially, the predicted curve in Figure 2 could be obtained in one piece with a correct dGDP. One has to use the same definition of GDP before and after 1978.

Beware of the BEA! It is not a surprise that economists can not find any clear relationships between macroeconomic variables - they are wrongly measured and misrepresented.

Figure 2. Observed and predicted rate of unemployment in the US.

Some corrections to David Altig's job market charts

2011-10-11T22:44:00.000+02:00

David Altig presented some projections of the unemployment rate based on various monthly increments in employment. It was a crude estimate because it did not include inherent fluctuations in the growth of working age population and labor force participation rate. It is much better to use Okun’s law linking unemployment and the real GDP growth.

Previously in this blog, we presented a version of Okun’s law for the rate of unemployment in the USA since 1955 as defined by real GDP per capita. We have estimated Okun’s law coefficients in two different segments using a standard LSQ technique. The reason behind the split into two segments was the change in realGDP estimation procedure introduced around 1978 - the definition of the GDP deflator was dramatically changed. We discussed this important methodical issuein our blog.

The best-fit (dynamic) model minimizing the RMS error of the cumulative model is as follows:

du = -0.406dlnG + 1.113, t<1979

du = -0.465dlnG + 0.866, t>1978

This model suggests a smaller shift in the slope and a larger change in the intercept around 1979. This Okun’s law is characterized by a standard error of 0.53% for the period between 1958 and 2010. The average rate of unemployment for the same period is 5.6% with an average annual increment of 1.06%.

Using the relationship for the period after 1979, one can estimate the evolution of the unemployment rate for various growth rates o real GDP per capita. We have selected three different values: 1% per year, 1.86% per year, and 3% per year. The first value is approximately equal to the mean growth rate between 2000 and 2010 (11 years) which is 0.93% per year. The second value provides a constant rate of unemployment, as defined by the ratio of coefficients 0.866/0.465 and is slightly higher than the mean rate after 1980 (1.63% per year). The third value is very high and just demonstrates the condition to reduce the rate of unemployment to 4% by 2020. Figure 1 depicts the predicted and observed rate of unemployment after 1980 and these three projections. This is a more accurate projection than that by David Altig.

I do not see any opportunity for the rate of unemployment to fall any time soon. In the long run, unemployment will remain high. For the slow growth scenario expected by the FRB, u may reach 13% by 2020.

Figure 1. Observed, predicted and projected rate of unemployment in the USA.

Procter and Gamble - stable share price in Q4 2011

2011-10-11T15:47:00.000+02:00

In this post, we revisit a share price model for Procter and Gamble as based on the decomposition into a weighted sum of two consumer price indices (to be determined), linear time trend and constant. It is shown that the model is valid since September 2009 at least and does not show any sign of possible failure. It predicts the share price at a four month horizon.

A share price model for Procter and Gamble (NYSE: PG) was originally published in this blog in July 2010. According to our concept, it was defined by the index of food away from home (SEFV - CUUS0000SEFV) and that of rent of primary residency (RPR); the evolution of these indices is presented in Figure 1. The former CPI component led the share price by 3 months and the latter one led by 8 months. The upper panel of Figure 2 depicts the original model and the monthly closing prices available in July 2010. This model was stable for the previous 11 months, i.e. for the period from September 2009.

In April 2010, we updated the original model using some new data (closing price for March 2011) and found that the same model was also applicable with a small change in the time lead for the SEFV – it was 4 months instead of 3 months in the original model. New coefficients were also slightly different, but very close to the original ones.

The most recent update uses the monthly closing price for September 2011 and CPIs for August 2011. It validates the model obtained for the previous period but is characterized by the same time lags and a small shift in the coefficients estimated by the LSQ technique. Three best-fit models for PG(t) are as follows:

PG(t) = -5.88SEFV(t-3) + 3.43RPR(t-8) + 17.60(t-1990) + 174.08, July 2010

PG(t) = -5.40SEFV(t-4) + 2.93RPR(t-8) + 18.16(t-1990) + 187.47, March 2011

PG(t) = -4.94SEFV(t-4) + 2.47RPR(t-8) + 18.15(t-1990) + 184.89, September 2011

where PG(t) is the monthly closing price (dividend and split adjusted) in US dollars, t is calendar time.

In the lower panel of Figure 2, the predicted curve leads the observed price by 4 months with the residual error of $2.12 for the period between July 2003 and September 2011 (see Figure 3 for the model residuals). In other words, the price of a PG share is completely defined by the behaviour of these two CPI components.

The model does predict the share price in the past and foresees a period of no growth in the fourth quarter of 2011. In January 2012, the price may fall, but we should revise the model with new data by that time.

Figure 1. Evolution of the price of SEFV and RPR.

Figure 2. Observed and predicted PG share prices. In the upper panel, the original prediction published in July 2010 with a three month lead shown by red line. The middle panel – the model published in April 2011. The lower panel – the most recent model with the monthly closing price for September 2011.

Figure 3. The model residual error.

Loews Corporation share price in 2011 Q4

2011-10-10T21:34:00.001+02:00

Half a year ago, we presented a share price model for Loews Corporation (NYSE: L) based on the decomposition into a weighted sum of two consumer price indices (selected from a larger number of CPIs), linear trend and constant, all coefficients and time lags to be estimated by a LSQ procedure. Here we test the previous model and make a regular update using new data. All in all, the original model is valid since October 2008 and does not show any sign of future changes. This is a reliable model valid during the past 47 months!

A preliminary model for Loews Corp. was obtained in September 2009 and covered the period from October 2008. This old model included the index of food without beverages (FB) and the index of transportation service (TS). The most recent model also used the monthly closing prices as of April 2011 and the CPI estimates published on April 14, 2011. The defining indices were almost the same: the index of food (F) and the TS index. Figure 1 depicts the evolution of the indices which provide the best fit model, i.e. the lowermost RMS residual error, between October 2009 and September 2011. The F index leads by 5 months and the TS index by 4 months. When new data through September 2011 are used, the model does not show any tangible change - only coefficients have been slightly drifting:

L(t) = -2.03F(t-5) – 2.12TS(t-4) +28.23(t-1990) + 448.98, March 2011

L(t) = -2.01F(t-5) – 2.09TS(t-4) +27.96(t-1990) +440.65, September 2011

where L(t) is the share price in US dollars, t is calendar time. The new model is depicted in Figure 2 together with high and low monthly prices as a proxy to the uncertainty bound of the share price. The predicted curve leads the observed one by 4 months. The residual error is of $2.38 for the period between July 2003 and September 2011. In the fourth quarter of 2011, the model foresees a very slight increase. The model obtained in March 2011, accurately predicted the fall observed in the second and third quarters.

Figure 1. Evolution of the price indices F and TS.

Figure 2. Observed and predicted share prices.

On the evolution of Forest Laboratories stock prices

2011-10-09T20:31:00.001+02:00

During the past two days, we re-estimated share price models for a few companies from the S&P 500 list and found them reliable and accurate for a year and even longer. Forest Laboratories (NYSE: FRX) is one of the first companies with a stable and deterministic share price modelestimated in September 2009 (this early model tracks back into November 2008). This is a company from Healthcare subcategory specialized in drugs manufacturing. We revisited this model in September 2010, December 2010, and March 2011 and always found the same defining variables with almost the same time lags.

Our approach to deterministic share pricing is based on the decomposition of a share price into a weighted sum of two selected consumer price indices. For FRX, all models between 2009 and 2011 are defined by the (not seasonally adjusted) index of dairy and related products (DAIRY) and the price index of other household equipment and furnishing (OHEF), as reported by the US BLS. In the September 2011 model, the former CPI component leads the share price by 4 month and the latter is 5 months ahead of the share price. Figure 1 depicts the overall evolution of both involved indices through August 2011.

Thus, three models mentioned above are as follows:

FRX(t) = -0.53DAIRY(t-3) – 4.18OHEF(t-5) - 13.41(t-1990) + 706.57 (September 2009)

FRX(t) = -0.61DAIRY(t-3) – 3.89OHEF(t-4) - 11.65(t-1990) + 668.75 (March 2011)

FRX(t) = -0.66DAIRY(t-4) – 3.45OHEF(t-5) - 9.78(t-1990) + 613.99 (September 2011)

where t is calendar time. All coefficients have been slightly drifting due to then trade-off between the linear time trend and the trend in the difference between the CPIs and due to new data. Therefore, the model is effectively the same between November 2008 and September 2011. In other words, we obtained a deterministic (leading by three months) model which was valid during 36(!) months. Figure 2 depicts the predicted and observed prices together with the relevant monthly highs and lows. The residual error is $4.75 (see Figure 3) for the period between June 2003 and September 2011 ($4.40min March 2011). From the most recent model in Figure 2, we expect a fall to the level of $20 per share in the fourth quarter of 2011.

Figure 1. Evolution of the price of DAIRY and OHEF.

Figure 2. Observed and predicted FRX share prices.

Figure 3. The model residual.

BofA, how are you doing?

2011-10-09T17:53:00.000+02:00

Twenty months ago we presented a preliminary share price model for Bank of America (NYSE: BAC). It was based on the decomposition of the price into a weighted sum of two consumer price components, a linear time trend and a constant (see details here). All coefficients, time lags and CPIs were estimated by the least squares as applied to the model error. Here we revisit the model using new data through September 2011. The newly estimated model differs from the preliminary one due to strict constraints on time lags and new data.

The preliminary model was a defined by the index of food without beverages (FB) and that of food away from home (SEFV). The latter CPI component leads by 13 months. From our past experience, the larger is the lag the more unreliable is the model. So it happened to the preliminary model and the best-fit two component model for BAC(t) is as follows:

BAC(t)= -2.35OHF(t) + 1.19H(t) + 1.97(t-1990) + 165.79

Figure 2 depicts the observed and predicted prices and indicates that the market overestimates the share above the predicted level. The overestimation is also clear from Figure 3 where the model residual is displayed. All in all, Bank of America might have serious problems, which could bring its share price down to $1 in the near future. Similar problems had Lehman Brothers and other banks several months before bankruptcy. BofA aslo was a bankrupt, but was bailed out.

Figure 1. The evolution of the defining consumer price indices.

Figure 2. Observed and predicted BAC share prices.

Figure 3. The model error

Aflac may grow to $45 in December 2011

2011-10-09T10:19:00.004+02:00

Here we revisit the stock price model for Aflac Incorporated (AFL) which we obtained in April2011 using our concept of share pricing. Accordingly, our goal is to test the original model and to update time lags and coefficients.

Last time we predicted the AFL price using the CPI estimates published by the BLS for March 2011. It was a preliminary model. The share price was defined by the consumer price index of household furnishing and operations (HFO) and that transportation services (TS). The defining time lags are as follows: the HFO index led the share price by 2 months and the TS by 4 months. Adding new data for the period between April and September 2011, we re-estimated the model and found some changes in the time lags: zero and five months, respectively; and in the estimated coefficients. The relevant best-fit 2-C models for AFL(t) are as follows:

AFL(t) = -5.02HFO(t-2) – 2.87TS(t-6) + 20.42(t-1990) + 997.71, March 2011

AFL(t) = -4.63HFO(t-0) – 2.90TS(t-5) + 20.41(t-1990) + 953.49, September 2011

where AFL(t) is the AFL share price in U.S. dollars, t is calendar time. The changes in time lags are shown in red.

In July2011, we reported that the original model gave a correct prediction of the fall in Q2 2011. Here we show that the current fall in the price has to stop and expect a positive correction in Q4 2011. Figure 1 depicts the high and low monthly prices for an AFL share together with the predicted and measured monthly closing prices (adjusted for dividends and splits). As a rule, the predicted prices are well within the bounds of the share price uncertainty. However, the price has fallen too much in Q3 and the model residual error (Figure 2) is negative what indicates a positive correction any time soon, if the model is right.

Figure 1. Observed and predicted AFL share prices.

Figure 2. The model residual error.

Alcoa share price may fall to $5 in December

2011-10-09T09:41:00.002+02:00

Six months ago we reported a share price model for Alcoa (AA), which is company from Materials subcategory of the S&P 500 list specialized in aluminum. Here we test and update the model using new data, including the monthly closing price in September 2011 and the estimated CPI components for August. The principal result is that we accurately predicted the behavior in Q2 and Q3 and the updated model has the same defining CPI components and time lags with slightly shifted coefficients. Therefore, the model is a reliable tool to predict the evolution of Alcoa at a two month horizon.

The original and current AA model is defined by the (not seasonally adjusted) index of food away from home (SEFV) and the price index of rent of primary residence (RPR), as reported by the US BLS. The former CPI component leads the share price by 2 months and the latter is 4 months ahead of the share price. Figure 1 depicts the overall evolution of both involved indices through August 2011. It seems these indices have been evolving in sync since 2002 with the only step-like change in the SEFV index in 2008. We present two empirical models as estimated in April and October 2011:

AA(t) = -6.71SEFV(t-2) + 3.34RPR(t-4) + 19.23(t-1990) + 298.87, Aril 2011

AA(t) = -6.61SEFV(t-2) + 3.22RPR(t-4) + 19.51(t-1990) + 300,89, October 2011

where AA(t) is a share price in US dollars, t is calendar time. Figure 2 illustrates the observed and predicted models for October 2011. The residual error is $3.04 ($3.12 in April) for the period between July 2003 and September 2011. Figure 2 also shows monthly high and low prices as the uncertainty in the monthly closing price as the best share price estimate. Since the closing price has to characterise the whole month by one value the high and low prices might serve as statistical bounds.

One can expect the share price to fall to the level of $5 in December 2011.

Figure 1. Evolution of the price of SEVF and RPR.

Figure 2. Observed and predicted AA share prices.

Predicting Harley-Davidson share price

2011-10-08T21:10:00.002+02:00

Harley-Davidson (HOG) is one of the best illustrations of our concept (see a brief description of the concept in Appendix) linking stock prices to CPI components. For HOG, the model is stable for many years. The first model was obtained in September 2009 and covered the period from October 2008. Here we revisit the HOG model using the monthly closing price for September 2001 and the CPI estimates published for August 2011.

HOG(t) = -13.82RPR(t-3) +12.77ORPR(t-4) +17.82(t-1990) – 163.94, before September 2009

HOG(t) = -11.30RPR(t-3) + 9.83ORPR(t-3) +17.53(t-1990) – 36.34, July 2011

HOG(t) = -11.27RPR(t-3) + 9.55ORPR(t-3) +19.35(t-1990) – 8.57, September 2011

where HOG(t) is the share price in US dollars, t is calendar time. The model is characterised by standard deviation of $4.33 for the period between July 2003 and September 2011.

Two recent models are depicted in Figure 2. The predicted curves lead the observed ones by 3 months. We do not foresee any further fall in the stock price. Figure 3 displays the residual error.

Figure 1. Evolution of the price indices ORPR and RPR.

Figure 2. Observed and predicted POM share prices. Upper panel – the model for March 2011. Lower panel – the model for September 2011.

Figure 3. The model residual error.

Appendix

In its general form, our pricing model is as follows:

sp(t_j) = Σb_i∙CPI_i(t_j-t_i) + c∙(t_j-1990 ) + d + e_j (1)

Hewlett Packard should not fall below $20 per share

2011-10-08T17:11:00.002+02:00

Hewlett Packard (HPQ) provides a good example of a successful share price prediction at a several month horizon. We have already published our predictions at a four month horizon four times (July 2010, January 2011, March 2011, and July 2011). All predictions were based on our concept of share pricing as decomposition into a weighted sum of two CPI components. We calculated the evolution of the monthly closing price (adjusted for dividends and splits). Here we test and update the model using data through September 2011.

Originally, the long term model for HPQ share price was defined by the index of food without beverages (FB) and that of rent of primary residency (RPR). The former CPI component led the share price by 4 months and the latter one led by 5 months. Figure 1 depicts the overall evolution of both involved indices through August 2011. Below we present three best-fit 2-C models for HPQ(t) obtained at different times:

HPQ(t) = -3.20FB(t-4) + 2.91RPR(t-5) + 3.64(t-1990) - 50.82, July 2010

HPQ(t) = -3.34FB(t-4) + 3.41RPR(t-5) + 0.51(t-1990) - 85.44, June 2011

HPQ(t) = -3.46FB(t-4) + 3.68RPR(t-5) – 0.72(t-1990) - 99.88, September 2011

where HPQ(t) is the price in US dollars, t is calendar time. All coefficients have been slightly drifting. This process expresses the trade-off between the linear trend in the difference between the defining CPIs and the time trend term in the above equtions.

The predicted curves are shown in Figure 2 (March and September 2011). In the second quarter of 2011, the model predicted the share price to fall to the level of $37 in June 2011 and then to $33 by the end of July 2011.

From Figure 2, we predict the price to stabilize around $20 because the current price level is below the predicted one. Therefore, one can expect the price not to drop below $20 per share.

Figure 1. Evolution of the price of FB and RPR.

Figure 2. Observed and predicted HPQ share prices in March (upper panel) and September (lower panel) 2011. The contemporaneous prediction is shown by red line. In March, we expect the price to fall down to $33 in July 2011. In September, we predict the price to stabilize around $20.

Is Boston Scientific on the brink?

2011-10-08T12:57:00.000+02:00

We posted on Boston Scientific (BSX) in January and April 2011 and presented a share price model for Boston Scientific as based on our stock pricing concept. Both models were similar and included the consumer price index of housing (H) and the index of durable goods (DUR). (Figure 1 depicts the overall evolution of the involved indices.) The former defining CPI component led the share price by 5 month and the latter one by 3 months. Here we update the original model using data through September 2011. The updated model has the same defining components and time lags with slightly different coefficients. Therefore, the original model provided a reliable prediction through the past year and further in the past. Currently, the best fit model predicts the share to fall below zero in the near future what is equivalent to bankruptcy. A similar prediction (negative share price) was obtained for Lehman Brothers and other financial institutions before they failed. As an alternative, our model may fail with the change in the overall CPI trends.

The best-fit 2-C models (March and September) for BSX(t) are as follows:

BSX(t) = -1.41H(t-5) – 2.85DUR(t-3) – 0.09(t-1990) + 630.92, March 2011

BSX(t) = -1.46H(t-5) – 2.71DUR(t-3) + 0.36(t-1990) + 615.69, September 2011

Both models predicted the price at a three month horizon with standard deviation of $1.94 between July 2003 and September 2011 ($1.83 in March). The currently observed growth in the defining consumer price indices should drive the share price down. In the fourth quarter of 2011, the price may drop below zero.

Figure 1. The evolution of H and DUR.

Figure 2. Observed and predicted BSX share prices. Upper panel: March model with red curve representing the contemporaneous prediction. Lower panel: the updated prediction. The price is negative by the end of 2011.

Figure 3. The model residual, i.e. the difference between the observed and predicted BSX share prices.

Avery Dennison share price will likely be falling further

2011-10-08T12:15:00.002+02:00

In April 2011, we presented a model for Avery Dennison Corporation (AVY) based on our concept linking share pieces and consumer price indices. The share price model for Avery Dennison Corporation was defined by the index of food (F) and that of new and used motor vehicle (NUMV). In the original model, the former CPI component led the share price by 4 months and the latter one led by 2 months.

Here we revisit the model using the monthly closing prices (adjusted for splits and dividends) and CPIs for the period through September 2011. (The CPIs are available only for August 2011.) The principal result is that the underlying model is practically the same as six months ago with the same time lags but slightly different coefficients. In March 2011, we predicted a fall in the price which actually happened. Currently, the share price is overestimated if to consider that the predicted price expresses the right behavior. We expect that AVY stocks will be falling by the end of 2011 down to $16 per share from the September closing level $25.08.

Figure 1 depicts the overall evolution of both involved indices between July 2005 and August 2011. These two defining components provide the best fit model between January 2010 and September 2011. Both models, the original and the updated one, are shown below. The best-fit 2-C models for AVY(t) are as follows

AVY(t) = -4.24F(t-4) – 3.23NUMV(t-2) + 23.29(t-1990) + 799.24 , March2011

AVY(t) = -3.92F(t-4) – 2.70NUMV(t-2) + 21.60(t-1990) + 710.60 , September 2011

where AVY(t) is a share price in US dolalrs, t is calendar time. Relevant coefficients are both negative. The slope of time trend is positive. There is some fluctuation in the coefficients caused by the uncertainty in measurements of both the stock prices and CPIs. Nevertheless, both models provide an accurate prediction at a two-month horizon.

The predicted curve in Figure 2 (both versions are depicted) leads the observed price by 2 months with the residual error of $2.57 ($2.68 in April) for the period between July 2003 and September 2011. The model residual for the same period is shown in Figure 3. The original model predicted the share price in the past and foresaw a fall in 2011 Q2.

Figure 1. Evolution of the price of F and NUMV.

Figure 2. Observed and predicted AVY share prices. Upper panel – March 2011; lower panel – September 2011.

Figure 3. The residual error of the model. The mean residual error is 0.0 with the standard deviation of $2.57. Currently, the price is slightly overestimated.

Another chance to sell oil futures

2011-10-06T20:45:00.004+02:00

Two weeks ago, when oil was at $84, I recommended to sell oil futures before oil price falls to $79 and even lower. After this recommendation, oil actually fell down to $76 and could bring a 10% return. Today, oil is approaching $83, as we predicted five days ago. Therefore, a good time to sell oil futures is coming again. Below I reproduce some details of the model predicting oil price.

In May 2011, we predicted oil (WTI) price to fall to the level of $70 per barrel by the end of 2011. This is a monthly revision for September 2011. We consider the average oil price of $84 per barrel what is equivalent to the producer price index of 244 in September. (Actual estimate will be published by the Bureau of Labor Statistics in the middle of October.)
Figure 1 compares our prediction with actual oil price in 2011. In August 2011, the predicted price is a bit higher than the measured one. In any case, we expect the price to fall by approximately $5 per month to the level of ~$70 in December 2011. We also expect the price to slowly fall through 2016 and put the uncertainty bounds for the long-term trend in oil price. The level of oil price in 2016 is between $30 and $60 per barrel. These bounds are also shown in Figure 1.

This part is the prediction of the current growth in oil price given days ago.

A week ago, when oil price was at ~$79 per barrel, we recommended buying oil futures. The intuition behind this idea was that $79 is approximately $5 below the expected price for September. This is a disequilibrium which should be recovered in the short run. Today, oil price is at the level of ~84. This is the equilibrium level for September. A small hike in oil price is possible during the next few days. However, at a two-week horizon, oil price should fall again. Therefore, I recommend selling now and buying in approximately two weeks or when the price will be around $75. It will grow to the level of ~$82 to $85 in October or November.

Figure 1. Oil price prediction in 2011. The price is expected to fall by $5 per month between June and December 2011. The price level is ~$70 in December 2011. We also show the range of expected price evolution by 2016.

Disappointing Bernanke

2011-10-05T15:44:00.000+02:00

Federal Reserve Chairman Ben Shalom Bernanke made several important statements in testimony to Congress's Joint Economic Committee that the Fed. In essence, they show the impotence of economic theory and thus economic authorities basing their policies on wrong understanding. Several examples:
1. “.. Recent revisions of government economic data show the recession as having been even deeper, and the recovery weaker, than previously estimated; indeed, by the second quarter of this year--the latest quarter for which official estimates are available--aggregate output in the United States still had not returned to the level that it had attained before the crisis.”

Any economics, financial or monetary policy should include some expected level of uncertainty in real time measurements such as real GDP and inflation (the GDP deflator). If it is always a surprise, how can one build a reasonable response and policy? One should never characterize an economy with one number without uncertainty. It contradicts scientific methodology.

2. “Slow economic growth has in turn led to slow rates of increase in jobs and household incomes.”

This statement presumes that there can be a situation when slow growth may lead to higher rates of increase in jobs and incomes. Actually, all these processes are equivalent and no one leads to another. They coexist.

3. “ Consumer behavior has both reflected and contributed to the slow pace of recovery.”

This statement is beyond any understanding. Consumers are treated as a black box without any rules how “garbage in” is converted into “garbage out”. This is a typical economic statement which explains every deviation in real economic growth as consumer behavior expressed in demand/supply shocks. Nobody knows what drives these shocks and why the economy runs away from the balance. In a way, this explanation creates a malice loop without start and end.

4. “Other sectors of the economy are also contributing to the slower-than-expected rate of expansion. The housing sector has been a significant driver of recovery from most recessions in the United States since World War II. This time, however, a number of factors--including the overhang of distressed and foreclosed properties, tight credit conditions for builders and potential homebuyers, and the large number of "underwater" mortgages (on which homeowners owe more than their homes are worth)--have left the rate of new home construction at only about one-third of its average level in recent decades. “

This deserves a special attention. Here Ben unfolds reasons one layer down. The housing sector slumps due to a number of factors. These factors are obvious results of the overall economic slump. What raises again the question on the reasons of the economic slump itself, and this is not housing as one can judge.

5. “ Nonetheless, financial stresses persist.”

Thus, the current financial crisis is a process which does not depend on real economic growth and when it is over, the economy will rocket up. Does that mean that the financial crisis could be healed without economic growth, but as it is? I would expect that the financial crisis will end when real economic growth recovers. In my opinion, it will happen in 5 to 10 years.

6. “In view of the deterioration in the economic outlook over the summer and the subdued inflation picture over the medium run ...”

This is a mere declaration of the status quo. However, the inflation projection is right as we showed many years ago