## 12/31/10

### Real GDP per capita in Japan

Couple months ago we presented the concept of constant annual increment in real GDP per capita, G(t), as observed in developed countries. The concept can be described by a simple model: in the long run, the GDP growth as a linear function of time:

G(t-t0)= G0+A(t-t0) (1)

where G0 is the initial level of GDP per capita at time t0 in a given country, A is the country dependent annual increment measured in PPP dollars. This is an empirical model and is based only on observations of real GDP in developed countries. This is in striking contrast to the mainstream macroeconomic models based on axioms; not empirically proved axioms.

Unlike in the Solow model and its successors, the rate of growth of real GDP per capita, dG/G, has a decelerating nonlinear trend. Differentiating with respect to time and dividing both sides of (1) by G(t), one obtains

dG/G = A/G (2)

This model gives excellent statistical results and explains the evolution of real GDP per capita in developed countries [1,2] since 1950. This year is considered as the year of relatively accurate measurements of GDP. We are using the data base provided by the Conference Board.

In the post related to labor productivity in Turkey, we introduced a model explaining the evolution of productivity as based on the deviation from constant annual increment of real GDP per capita. Therefore, model (1) provides an empirical framework for the productivity model and we need to illustrate the predictive power of (1).

Figure1 presents a very important case of Japan: annual increment in real GDP per capita is plotted against the level of real GDP per capita. (Equation (1) uses time implicitly.) It demonstrates the accuracy of our concepts. Since the increment is assumed to be constant, the mean value of the annual GDP increment should coincide (at least should be very close to) with its linear trend. The linear regression line for Japan is very close to the constant level. Actually, it slightly oscillates around the mean value over time, as the cases for 2007 (upper panel) and 2009 (lower panel) demonstrate. The hypothesis of the constant increment looks sound.

Figure 1. Annual increment of real GDP per capita (2007 and 2009 US\$) vs. real GDP per capita in Japan for the period between 1950 and 2007 (upper panel) and between 1950 and 2009 (lower panel). Two sets are presented - the original (open circles) and that corrected for population (filled diamonds). Subsequent values of the latter set are connected by a solid line for illustration of the evolution in time. Bold lines represent the mean value of \$605 (2007 US\$) and \$596 (2009 US\$) for the population corrected sets. Two solid lines show linear regressions lines. Corresponding linear relationships are displayed, the lower relationship being associated with the original data set.

Both original linear regression line is practically parallel to the x-axis. The corrected line is characterized by a slightly negative trend. There were two periods of very quick growth between \$12000 and \$20000 and between \$28000 and \$33000. Both ended in periods of low (sometimes - negative) growth rates. This effect might be expected in any country which demonstrates very fast growth during an extended period of time. A good example is Ireland. A candidate is China, but its growth is supported by the army of unemployed with very low salaries. Therefore, China may grow mainly due to extensive factors and real GDP per capita do not grow so fast as the overall GDP.

Following the general rule of the constant increment, one may expect a slow recovery of the Japanese economy over decades. However, this recovery is unlikely because the Japanese population is on long-term decline [3].

References
1. Kitov, I., (2006). Real GDP per capita in developed countries, MPRA Paper 2738, University Library of Munich, Germany, http://ideas.repec.org/p/pra/mprapa/2738.html

2. Kitov, I., (2009). The Evolution of Real GDP Per Capita in Developed Countries, Journal of Applied Economic Sciences, Spiru Haret University, Faculty of Financial Management and Accounting Craiova, vol. IV(1(8)_ Summ), pp. 221-234.

3. Kitov, I., 2006. "The Japanese economy," MPRA Paper 2737, University Library of Munich, Germany

## 12/29/10

### Does crude drive the price index of steel and iron?

This update includes the readings of the producer price indexes of crude oil and iron&steel for November 2010.
In September 2009, we reported that the price index of crude oil had been likely evolving in sync with that of iron and steel, but with a lag of two months [1].  In order to present both indexes in a comparable form, the difference between a given index, iPPI, and the overall PPI was normalized to the PPI: (iPPI(t)-PPI(t))/PPI(t). The normalized differences represent the evolution of the rate of deviation from the PPI over years.
Figure 1 depicts the corresponding time histories of the normalized deviations from the PPI, including the most recent period since June 2010.  Simple visual inspection reveals the following feature: the (normalized deviation from the PPI of the) index of iron and steel lags by two months behind the (normalized) index of crude oil.

Figure 1. The deviation of the iron and steel price index and the index of crude oil from the PPI, normalized to the PPI.

In order to reduce both deviations to the same scale we additionally normalized the curves in Figure 1 to their peak values between 2005 and 2010
(iPPI(t)-PPI(t))/[PPI(t)*max{iPPI-PPI)}]
This scaling allows a direct comparison of corresponding shapes. In Figure 2, we display the normalized index of iron and steel shifted by two months ahead to synchronize its peak with that observed in the normalized index for crude petroleum. The scaled index of crude demonstrates just short-term deviations from the index of iron and steel in the overall shape and timing of the peak and trough. Simple smoothing with MA(3) makes the curves resemblance even better. As an invaluable benefit of the resemblance, one can use the two-month lag to predict the future of the iron and steel price index.

Figure 2. Deviation of the iron and steel price index from the PPI, normalized to the PPI and the peak value after 2005 as compared to the deviations of the index for crude petroleum normalized in the same way. The normalized index for iron and steel is shifted two months ahead.

Conclusion
Between 2006 and 2010, the deviation of the price index of iron and steel from the PPI in the USA repeats the trajectory of the deviation of the index of crude petroleum (domestic production) with a two-month lag. Therefore, the prediction of iron and steel price for at this horizon is a straightforward one.

References
1. Kitov, I., Kitov, O., (2009). Sustainable trends in producer price indices, Spiru Haret University, Faculty of Financial Management and Accounting Craiova, vol. I(1(1)_ Summ), pp. 43-51

### Labor productivity in Austria: further fall in 2010 and 2011

As in the previous post, Figure 1 is borrowed from our paper on productivity [1] (see Figure 4 in the paper). It presents the case of Austria. This is a less difficult example with the rate of productivity growth, dP/P, on a steady descent since the 1970s.  Between 1975 and 2005, the rate of productivity growth is oscillating around the level of 0.015y-1. Notice the excellent prediction of the severe drop in the productivity after 1970.  This fall was induced by an increase in the growth rate of real GDP per capita relative to its inertial level, as Figure 2 depicts. The elevated rate of real growth induced a higher increase in the rate of participation, and thus, the drop in productivity.  It is worth stressing again that there was no shock to productivity or a structural break, as the mainstream economists would suggest. Our model presumes that labor productivity in Austria has been following the only driving force – real GDP per capita.

Figure 1. Observed and predicted (from real GDP pee capita) change rate of productivity in Austria.  The observed curve is represented by MA(5) of original version. Model parameters are as follows: A2=\$335, N(1959)=100000, B=-500000, C=0.243, T=3 year.
Lets return to the deviation from the inertial growth, which is unambiguously determined by constant annual increment of real GDP per capita. Figure 2 shows that the rate of inertial growth is decreasing with the increasing level of GDP as a reciprocal function of GDP.  Coefficient A2 has to be determined empirically for each developed country. For Austria, the initial estimate was A2=\$335 (1990 U.S. dollars at GK PPPs as presented by the Conference Board). The current economic and financial crisis manifests itself in a severe drop in GDP, with dGDP/GDP=-0.045 y-1 in 2009.

A significant feature of the model is the presence of a delay between the change in real GDP and the reaction of P. This effect is similar to the delay of thunder relative to lightning. Any economic system needs some time to adjust to the exogenous change. In Austria, productivity lags by 3 years behind GDP, as caption of Figure 1 indicates. For details of the model see [2]. For the purpose of this blog, the three year lag means that the current drop in real GDP per capita will result in a hike in labor productivity three years later. Also, the currently observed decline in the rate of productivity growth is actually induced by several years of intensive real economic growth observed before 2009.

Figure 2. Comparison of the growth rate of real GDP per capita, dGDP/GDP, with the rate of inertial growth defined as A2/GDP.

Finally, Figure 3 tests the model by adding two new data points to Figure 1. These new measurements are borrowed from the Conference Board database [3]. One can conclude that the model gave an excellent prediction for 2008 and 2009. The period of the productivity decline will continue in Austria for another couple years. Since 2012, the rate of productivity growth will show high positive values in response to the current drop in GDP and labor force particiaption. This will be a striking upturn which is always a challenge to any productivity model or concept. We will revisit the case of Austria for further validation of the model. In 2010 and 2011 the rate of labor productivity in Austria will be falling.

Figure 3. Same as in Figure 1 with two new points – 2008 and 2009. The original and MA(5) productivity series are shown. One can expect positive rate of productivity growth in 2012.

References
1. Kitov, I., Kitov, O., (2009). Modelling and predicting labor force productivity, MPRA Paper 15152, University Library of Munich, Germany, http://mpra.ub.uni-muenchen.de/15152/01/MPRA_paper_15152.pdf
2. Kitov, I., Kitov, O., (2008). The driving force of labor productivity, MPRA Paper 9069, University Library of Munich, Germany, http://ideas.repec.org/p/pra/mprapa/9069.html
3. Conference Board. (2010). Total Economy Database, January 2010. http://www.conference-board.org/data/economydatabase/

## 12/28/10

### On further decline in labor productivity in Turkey

Figure 1 is borrowed from our paper on productivity [1]. It presents the case of Turkey. This is a difficult example with the rate of productivity growth oscillating since 1980. Since the measured time series is smoothed with MA(3), actual oscillation is even more prominent. Such a behavior is a nightmare for the mainstream models based on capital, labor and multifactor productivity. As a rule, the multifactor productivity has to resemble observations and severe “shocks” to productivity are introduced.  This is a lucky hour for an economist – millions of factors to explain these shocks. In reality, the number of explanations is steadily approaching the number of economists involved. At the end of the day, all mainstream models are able to explain only “stylised facts”. This is a euphemism of “failure”.

Our model uses only one variable – real GDP per capita. The intuition behind the model is almost banal.
1. A developed economy is characterized by a constant speed of real economics growth, which we call “economic inertia” in line with mechanical sense of inertia. In other words, the economy would be growing with constant increment per year, i.e. at constant speed, if no change in the population age structure is observed.
2. Any deviation from the inertial growth results in the change in labor force participation. Obviously, a higher speed of growth may attract more people into the labor.
3. The number of people who are able to join the labor force in response to a given growth above the inertial one is proportional to the relevant deviation.
4. The value added by any newcomer must depend on his/her overall professional capabilities. It is obvious that this characteristic (capability or productivity) is distributed (we claim that this distribution is exponential and personal income distribution is) over the working age population and people with efficiency between 50% and 51 % should bring more value added to the economy than those between 75% and 76%.  In other words, one per cent of “extra” (above the inertial level) economic growth may allow to join the labor, say, 1% of population, when this labor force grows from 50% to 51% , or 5% of population, when the labor grows from 70% to 75%.  These portion must give the same extra input into the real GDP.
5. The extra growth in real GDP has to be reflected in productivity, which is defined as a ratio of real GDP and the level of labor force. As suggested in point 4, the extra labor force depends on the current participation rate. Therefore, the growth in productivity depends on the current rate of participation in labor force for a given increase in real GDP.  As an example, the rate of participation in Italy and Canada is quite different and 1% extra growth in real GDP per capita results in absolutely different change in labor productivity.
6. Mathematical formulation of this simple consideration is given in [2].

Finally, Figure 1 (and the example of Canada ) demonstrate the predictive power of our simple and parsimonious model.

Figure 1. Observed and predicted (from real GDP pee capita) change rate of productivity in Turkey.  The observed curve is represented by MA(3) of the original version. Model parameters are as follows: A2=\$105, N(1959)=1450000, B=-6000000, C=0.24, T=2 year.

Figure 2 tests the model by adding two new data points to Figure1. These new measurements are borrowed from the Conference Board database [3]. One can conclude that the model gave an excellent prediction for 2008 and 2009. The period of the productivity decline will continue in Turkey for another couple years, and then it will start to grow again. This turn is a challenge for any productivity model or concept. We will revisit the case of Turkey for further validation of the model. Meanwhile, we would not expect good news about labor productivity from Turkey.

Figure 2. Same as in Figure 1 with two new points – 2008 and 2009.

References
1. Kitov, I., Kitov, O., (2009). Modelling and predicting labor force productivity, MPRA Paper 15152, University Library of Munich, Germany, http://mpra.ub.uni-muenchen.de/15152/01/MPRA_paper_15152.pdf
2. Kitov, I., Kitov, O., (2008). The driving force of labor productivity, MPRA Paper 9069, University Library of Munich, Germany, http://ideas.repec.org/p/pra/mprapa/9069.html
3. Conference Board. (2010). Total Economy Database, January 2010. http://www.conference-board.org/data/economydatabase/

## 12/27/10

### Cross-country comparison: labor force participation in Canada and Italy

One of the most important requirements to a sound macroeconomic model is the capability to explain the difference in evolution of modelled parameters across developed countries.  For example, a consistent model of the rate of participation in labour force, LFP, has to describe the striking difference observed in the long-term behaviour of LFP in Canada and Italy. Figures 1 and 2 depict the measured (open circless), as provided by the BLS: http://www.bls.gov/data/, and predicted LFP. The latter is obtained from the model linking LFP to real GDP per capita only [1]. The GDP estimates are taken from the Conference Board data base (at GK PPPs).

Our model shows an exceptional predictive power for both countries. This accurate prediction is obtained despite the measured LFP in Canada has been growing since 1960 and that in Italy has been on decline.  Moreover, even short-term deviations from the overall trend are well predicted in time and amplitude. In Figures 1 and 2 we added two new measurements made in 2008 and 2009 to the original curves published in [1].

One can conclude that the model does not contradict actual measurements in Sweden, Canada, and Italy.  We are going to extend the set of countries and the duration of relevant time series.

Figure 1. Measured and predicted LFP in Canada.

Figure 2. Measured and predicted LFP in Italy.
References
1. Ivan O. KITOV, 2008. "The Driving Force of Labor Force Participation in Developed Countries," Journal of Applied Economic Sciences, Spiru Haret University, Faculty of Financial Management and Accounting Craiova, vol. 3(3(5)_Fall), pages 203-222.

### Labor force participation in Sweden

The labor productivity model discussed in the previous post is based on the concept linking labor force participation rate, LFP, to real GDP per capita [1]. This is a primary model, which explains the dynamics and the long-term behavior of labor force level in developed countries. As before, the LFP model is extremely parsimonious and uses only one (!) defining parameter to explain all variations in the observed behaviou of labor force in developed countries. As a consequence, one needs no other macro- or micro-economic variable to explain the portion of labor in total population.
In this post, we do not formally introduce the quantitative model since it is available in the paper and monograph. Our purpose is to extend the previous data set by two years (2008 and 2009) since new observations are now available. This is in line with our validation strategy – to test all models with new data.
Figure 3.13 is borrowed from our monograph and illustrates the predictive power of the model for Sweden. The agreement between the original LFP estimates (open circles) and those predicted by the model is excellent in timing and amplitude. Considering the fact that only one defining variable is used the prediction suggests the presence of long-term on-to-one link between LFP and real GDP. (More examples in the paper and monograph.)
Figure 1 extends the original data set by two estimates (real GDP per capita reported by the Conference Board). The agreement is also excellent.  This observation evidences in favor of our model.
We will continue reporting the accuracy of LFP predictions for Sweden and other developed countries.

Figure 3.13. Observed and predicted growth rate of LFP in Sweden: N(1959)=100000, A2=\$310 (1990 U.S. dollars), B=2.2∙106, C=-0.0465, T=0. Lower panel depicts the original LFP, changing in the range from 67% in 1990 to 62.5 % in 1998, and the predicted LFP.

Figure 1. Same as in Figure 3.13, but extended with data in 2008 and 2009. The original LFP series is  reported by the BLS.

References
1. Ivan O. KITOV, 2008. "The Driving Force of Labor Force Participation in Developed Countries," Journal of Applied Economic Sciences, Spiru Haret University, Faculty of Financial Management and Accounting Craiova, vol. 3(3(5)_Fall), pages 203-222.

## 12/26/10

Two years ago we published two papers [1,2] which introduced a new macroeconomic model explaining the evolution of labor productivity in developed countries. The model is absolutely parsimonious and uses only one measured macroeconomic variable as the driving force of the productivity growth – real GDP per capita. Figure 3.22 is borrowed from our monograph “mechanomics. Economics as Classical Mechanics” and illustrates the predictive power of the model as applied to Canada. (Due to extremely high volatility of productivity measurements, the measured data set is represented by its 5-year moving average, MA(5)).  All coefficients in the model for Canada were obtained empirically, as explained in the monograph.

Considering the simplicity of the model and the accuracy of data on real GDP and productivity, the prediction of the time history in Canada is excellent. (We would be grateful if the reader could provide us with a reference to a model which gives better predictions.) It is also important that the prediction covers the whole period since 1960 with one deterministic link without any structural breaks. The latter is the inevitable and crucial element of any explanation of productivity in developed countries. Moreover, all mainstream macroeconomic models (e.g. DGSE) are using the notion of shocks to productivity as a central phenomenon explaining all bigger deviations in the rate of real economic growth. This implies that productivity must define real GDP. This assumption contradicts observations, as our model demonstrates – the change in labor force productivity lags by two (!) years behind the defining change in real GDP. Therefore, productivity is not a proactive macroeconomic variable.

Figure 3.22. Observed and predicted productivity in Canada: N(1959)=270000, A2=\$300, B=-3200000, C=0.108; R2=0.8.

Since the data set was limited by 2007, one can test the predictive power of the model using new data and extend the forecasting horizon. As before, we use the data set published by the Conference Board [3].  Figure 1 shows that our prediction for 2008 and 2009 was accurate. In the near future, one can expect a significant growth in labor productivity in Canada.

For further validation of the model, we are going to revisit our predictions for other developed countries.

Figure 1. Same as in Figure 3.22 extended by measurements in 2008 and 2009.

References
1. Kitov, I., Kitov, O., (2008). The driving force of labor productivity, MPRA Paper 9069, University Library of Munich, Germany, http://ideas.repec.org/p/pra/mprapa/9069.html

2. Kitov, I., Kitov, O., (2009). Modelling and predicting labor force productivity, MPRA Paper 15152, University Library of Munich, Germany, http://mpra.ub.uni-muenchen.de/15152/01/MPRA_paper_15152.pdf

3. Conference Board. (2010). Total Economy Database, January 2010. http://www.conference-board.org/data/economydatabase/

## 12/25/10

### CPI and core CPI

Following our previous post on the difference between the headline CPI and GDP deflator we have to revisit the difference between the overall CPI and core CPI (the headline CPI less food and energy). This difference also demonstrates severe changes in definitions of all involved variables and reveals a break in these time series, which harms the compatibility of CPI measurements before and after 1979.
Two years ago we published a paper on the presence of long-term sustainable trends in the differences between various components of the CPI in the USA. We started with the difference between the core CPI (i.e. CPI less food and energy) and the overall CPI. Figure 1 is similar to Figures 1 and 2 in the paper.
Figure 1. Linear regression of the difference between the core CPI and CPI for the period from 1981 to 1999 (R2= 0.96 the slope is 0.67) and linear regression of the difference between the core CPI and CPI between 2002 and 2009 (R2=0.91, and the slope is -1.59).

We also suggested in this and later papers on the sustainable trends in the CPI and PPI (see here) that the negative trend after 2002 should reach some bottom point and turn to a positive trend. It was also mentioned that such processes in the past had been accompanied by an elevated volatility in the difference, i.e. high amplitude fluctuations. All predictions were actually observed. Figure 2 updates Figure 1 with data available in November 2010.

Figure 2. A new positive trend has been emerging since 2010.

Therefore, we confirm our previous predictions and expect the new positive trend in Figure 2 shows. This trend repeats the trend observed between 1987 and 1999 rather than the mirror reflection of the previous negative trend between 2002 and 2009, as was suggested before. Thus, the price indices of food and energy will not be falling too fast relative to the core CPI, but this period will likely last more than 10 years. We will keep posting on the difference

## 12/24/10

### Is real GDP correct?

This is an instructive story about the metrology of macroeconomic measurements. From the point of view of hard sciences, the economics profession does a great counterproductive work in order to hide actual links between macroeconomics variables. Among the most effective tools of this clandestine operation is the random change in definitions of these macroeconomics variables without mentioning it during statistical analysis.   We have devoted enough time to reveal and recover many trivial cases in our book “mecħanomics. Economic as Classical Mechanics”, but the list is too far to be closed yet.

It is well known that there is no such macroeconomic measurable parameter as real GDP (see Concepts and Methods of the U.S. NIPA for details). There are two actually measured variables: nominal GDP and GDP deflator (price index). Real GDP is estimated using nominal GDP less the change in prices. (General public usually thinks that there are real and nominal GDP estimated and the difference is called price inflation. Wrong.) The latter is not easy to calculate or even evaluate.  It is so much sophisticated problem that before 1980, there was no practical difference between the consumer price index (CPI) and the GDP deflator in the U.S., as Figure 1 demonstrates. Effectively, the curves in the Figure split in 1980. 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 new methodology to evaluate price inflation.

Thus, before 1980 the CPI was used as an estimate of price inflation. Since 1980, GDP deflator has been used. The difference between these two variables can not be neglected: the cumulative change in inflation between 1980 and 2009 is 20 points – the Figure shows cumulative change in inflation rate since 1929. Does that mean that when applied to the estimates before 1980, the concept of dGDP would result in even bigger change in real GDP estimates?

All in all, the notion of real GDP is a virtual one and is highly biased by the change in its definition in 1979.  One has to be very careful when using real GDP estimates in economic analysis. Do not trust the BEA before you check the comparability of their estimates through time.

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

## 12/21/10

### The price index of motor fuel

Couple years ago we introduced a concept of deterministic and sustainable trends in the differences of consumer (and later on - producer) price indices [1,2]. One of the best examples was the difference between the overall (often called “headline”) CPI and the index of motor oil [3], both indices were seasonally adjusted ones. A year ago we revisited this difference [4] and found our predictions in good agreement with observations. Essentially, this difference approached the new trend and we expected the following evolution along this new trend, which assumes that the price index of motor fuel will decrease relative to the CPI.

The model implies that the difference between the overall CPI (same for the PPI), CPI (PPI), and a given individual price index iCPI (iPPI), can be described by a linear time function over time intervals of several years:

CPI(t) – iCPI(t) = A + Bt (1)

, where A and B are the regression coefficients, and t is the elapsed time. Therefore, the “distance” between the CPI and the studied index is a linear function of time, with a positive or negative slope B. Free term A compensates the difference related to the start levels for a given year. For example, the index of communication was started from the level of 100 in December 1997 when the overall CPI was already at the level of 161.8 (base period 1982-84 =100).

This post displays the evolution of the difference in 2010 (see Figure 1). All in all, the prediction was good enough: the earlier positive deviation from the trend is now compensated by a negative one. Currently, the difference is below the trend and we expect it to reach the trend in the beginning of 2011. This recovery should be accompanied by a drop in the price index of motor fuel and likely in the index of crude oil.

Figure 1. The difference between the headline CPI and the index for motor fuel. Solid diamonds represent the prediction given in March 2009 through December 2009 [1]. The total increase in the difference is +60 units of index or +35%: from 173 in March to 233 in December. Dashed line represents the new trend, which is a mirror reflection to that between 2001 and 2008 shown by solid black line. In 2010, the difference has been fluctuating around the trend and thus should return to the trend in the beginning of 2011.

References
1. Kitov, I., Kitov, O. (2008). Long-Term Linear Trends In Consumer Price Indices, Journal of Applied Economic Sciences, Spiru Haret University, Faculty of Financial Management and Accounting Craiova, vol. III(2(4)_Summ), pp. 101-112.

2. Kitov, I., Kitov, O. (2009). Sustainable trends in producer price indices, Journal of Applied Research in Finance, vol. I(1(1)_ Summ), pp. 43-51.

3. Kitov, I., Kitov, O. (2009). A fair price for motor fuel in the United States, MPRA Paper 15039, University Library of Munich, Germany

4. Kitov, I.. Kitov, O. (2010). "Crude oil and motor fuel: Fair price revisited," Quantitative Finance Papers 1005.0051, arXiv.org.

### Bernanke and deflation

Deflation is the economic topic where the current  Chairman of the United States Federal Reserve Shalom Bernanke has got his nickname. We have been following the evolution of inflation in the USA for many years since predicted (2005)  that a deflationary period should start in 2012.  So, it is instructive to  compare the tools proposed by the Fed's chairman and actual situation. The most recent statement from the FRB on deflation was in the speech  on October 15.

... The significant moderation in price increases has been widespread across many categories of spending, as is evident from various measures that exclude the most extreme price movements in each period. For example, the so-called trimmed mean consumer price index (CPI) has risen by only 0.9 percent over the past 12 months, and a related measure, the median CPI, has increased by only 0.5 percent over the same period.2

... With long-run inflation expectations stable and with substantial resource slack continuing to restrain cost pressures, it seems likely that inflation trends will remain subdued for some time.
The longer-run inflation projections in the SEP indicate that FOMC participants generally judge the mandate-consistent inflation rate to be about 2 percent or a bit below. In contrast, as I noted earlier, recent readings on underlying inflation have been approximately 1 percent. Thus, in effect, inflation is running at rates that are too low relative to the levels that the Committee judges to be most consistent with the Federal Reserve's dual mandate in the longer run. In particular, at current rates of inflation, the constraint imposed by the zero lower bound on nominal interest rates is too tight (the short-term real interest rate is too high, given the state of the economy), and the risk of deflation is higher than desirable. Given that monetary policy works with a lag, the more relevant question is whether this situation is forecast to continue. In light of the recent decline in inflation, the degree of slack in the economy, and the relative stability of inflation expectations, it is reasonable to forecast that underlying inflation--setting aside the inevitable short-run volatility--will be less than the mandate-consistent inflation rate for some time. Of course, forecasts of inflation, as of other key economic variables, are uncertain and must be regularly updated with the arrival of new information.

﻿and finally

Given the Committee's objectives, there would appear--all else being equal--to be a case for further action. However, as I indicated earlier, one of the implications of a low-inflation environment is that policy is more likely to be constrained by the fact that nominal interest rates cannot be reduced below zero. Indeed, the Federal Reserve reduced its target for the federal funds rate to a range of 0 to 25 basis points almost two years ago, in December 2008. Further policy accommodation is certainly possible even with the overnight interest rate at zero, but nonconventional policies have costs and limitations that must be taken into account in judging whether and how aggressively they should be used ...

So, the problem of approacing price deflation now is a big one. To implememnt a helicopter ﻿technology is not so easy.

## 11/13/10

### Real GDP per capita in developed countries

Five years ago I published a paper [1] introducing the concept of constant annual increment in real GDP per capita, G(t), as observed in developed countries. In the long run, the GDP growth as a linear function of time:

G(t-t0)= G0+A(t-t0)

where G0 is the initial level of GDP per capita at time t0 in a given country, A is the country dependent increment measured in PPP dollars. Therefore, the rate of growth of real GDP per capita, dG/G, has a decelerating nonlinear trend:

dG/G = A/G

This assumption gives excellent statistical results and explains the evolution of real GDP per capita in developed countries, as also was confirmed in our 2008 paper [2].

Hence, the task now is to track the progress of the economies under study. The figure below presents several important cases, which demonstrate the accuracy of our concept. Since the increment is assumed to be constant, the mean value of the annual GDP increment should coincide with its linear trend (see the paper for details). In reality, the linear regression line is very close to the constant level. In many cases (e.g. France, Italy, Japan), it oscillates around the mean value over time with a small amplitude. The hypothesis of the constant increment looks sound.

Figure. The increment of real GDP per capita vs. real GDP per capita in select developed countries. Thick line – the mean increment. Two solid lines represent two linear trends (also represented by their equations) as associated with the original and population corrected GDP estimates. All data are borrowed from the Conference Board data base (http://www.conference-board.org/economics/database.cfm).

References
[1] Kitov, I., (2006). Real GDP per capita in developed countries, MPRA Paper 2738, University Library of Munich, Germany, http://ideas.repec.org/p/pra/mprapa/2738.html

[2] Kitov, I., (2009). The Evolution of Real GDP Per Capita in Developed Countries, Journal of Applied Economic Sciences, Spiru Haret University, Faculty of Financial Management and Accounting Craiova, vol. IV(1(8)_ Summ), pp. 221-234.

## 11/9/10

### Journal of Applied Economic Sciences. Fall 2010

JAES 3(13). / Fall 2010
Contents

Mongi ARFAOUI, Ezzeddine ABAOUB. On the Determinants of International Financial Integration  in The Global Business Area …153

Melita CHARITOU, Petros LOIS, Adamos VLITTIS, Do Capital Markets Value Earnings and Cash Flows Alike?  International Empirical Evidence … 173

Madalina CONSTANTINESCU, Laura UNGUREANU, Laura STEFANESCU. Portfolio Optimal Choice under Volatility and Price Risk Impact  Applied to Derivative Transactions … 184

Georg ERBER, The Problem of Money Illusion in Economics … 196

Marco FIORAMANTI, Estimation and Decomposition of Total Factor Productivity Growth in the EU Manufacturing Sector: A Long Run Perspective … 217

George E. HALKOS, Marianna K. TRIGONI, Financial and Real Sector Interactions: The Case of Greece … 231

Cosmin FRATOSTITEANU, Guidelines for Promoting Science, Technology and Technical–Scientific Creativity, By Analyzing the Companies’ Performances, in the Context of the Globalized Economy …247

Drama Bedi Guy HERVE, Yao SHEN, Management of Stock Price and its Effect on Economic Growth:  Case Study of West African Financial Markets … 258

Bernard LANDAIS, The Monetary Origins of the Economic and Financial Crisis … 280

Piotr MISZTAL, Public Debt and Economic Growth in the European Union …292

## 11/7/10

### Black Tuesday?

I assume that the closing S&P 500 level of 1183 in October 2010 and its following growth to 1225 in November 2010 is not good news for the US stock market. Figures 1 and 2 update the previous versions published in this blog in September. Both Figures demonstrate that the difference between the predicted and observed curves has been increasing since September.

This observation raises a question on the following events. Our concern about possible repetition of the 1987 fall, if the index would continue its deviation from the predicted trend into October 2010 is on again. So, I see a danger of a severe panic on the stock market. Because Tuesday is a common day for such events, I cannot exclude that one of Tuesdays in the nearest future will end in a return of the observed curves to the predicted one.

There is also a chance that the population estimates underlying the prediction become wrong since September 2010. The methods of population projection and updates used by the Census Bureau are also not well predicted.

Below we repeat a mandatory part with a bit of mathematics for the readers interested in details of our model. The model is also presented in our working paper [1] and monograph [2].

The original model links the S&P 500 annual returns, Rp(t), to the number of nine-year-olds, N9. In order to extend the prediction in time we use the number of three-year-olds, N3, as a proxy to N9 and obtain a forecast at a six-year horizon:

Rp(t+6) = 100dlnN3(t) - 0.23 (1)

where Rp(t+6)is the S&P 500 return six years ahead (in 2010 one can foresee the returns in 2016). Figure 1 depicts germane S&P 500 returns, both actual one and that predicted by relationship (1). Both curves are coinciding in practical terms.

Because of the observed linear growth in N3 one can replace it with linear trends for the period between 2008 and 2011, as Figure 2 shows. This model predicts that the S&P 500 stock market index will be gradually decreasing at an average rate of 37 points per month. All fluctuations in N3, as observed in Figure 1, are smoothed in this linear representation.

Figure 1. Observed and predicted S&P 500 returns. The last point for the observed series is October 31, 2010.

Figure 2. The observed monthly closing level of the S&P 500 stock market index and the trend predicted from the number of nine-year-olds. The slope is of -37 points per month. The same but positive slope was observed between February 2009 and April 2010. The last point in the observed series is October 31, 2010. The deviation between the predicted and observed curves has been increasing since September 2010.

## 11/5/10

### Monographs

We have published three monographs this year. All three reflect the problems and topics, which have always been the main topic of this blog: Economics as Classical Mechanics. If one has been following the blog from the very beginning s/he could have a great deal of knowledge about our concept and results. Random readers might be interested in specific topics from inequality to stock pricing.

Our publisher, the LAP Lambert Academic Publishing, encourages us and we are also happy to request a favour of all readers to help the overall promotion of these monographs. We would appreciate very much if you could leave a short review and/or evaluate the books on amazon.com:

Mechanics of personal income distribution: The probability to get rich

Deterministic mechanics of pricing

mechanomics: Economics as Classical Mechanics

Ivan Kitov

## 10/28/10

### Our brand-new monograph: Economics as Classical Mechanics

Finally, our book "mechanomics" or " Economics as Classical Mechancis" is published by the LAP Academic Publishing. We have collected all individual studies on macroeconomics issues published as working papers and journal articles and updated empirical relationships where possible.

The monograph is available on amazon.com:
http://www.amazon.com/mec-anomics-Economics-Classical-Mechanics/dp/3843361223/ref=sr_1_1?ie=UTF8&s=books&qid=1288264906&sr=8-1

Abstract
Macroeconomics is represented as a hard science like physics and, specifically, classical mechanics. Due to this similarity we have called our concept ?mec?anomics? highlighting its mechanistic entity. There exist statistically reliable deterministic links between measured macroeconomic variables. In the order of causality, the overall population and its age structure drives the evolution of real GDP which, in turn, determines the rate of participation in workforce. The level of labour force unambiguously defines the rate of price inflation and unemployment. The age structure also controls the S&P 500 returns. Statistically, the goodness-of-fit between measured and predicted macroeconomic time series is at the level of 0.9, with the residuals likely related to measurement errors. Tests for cointegration confirm the presence of long-term equilibrium relations. We have extended the sets of econometric tools by the method of boundary elements well-known in physics. As a bonus to the prolific concept of mec?anomics, we have modelled real GDP per capita during the transition from socialism to capitalism combining two physical processes: radioactive decay and saturation.

## 9/14/10

### 1987, 2001, 2008 … 2011

The essence of any quantitative model consists in the accuracy of prediction or predictive power. The higher is signal/noise ratio in a given data set the better can be the estimate of model parameters or the uncertainty of corresponding prediction. For the S&P 500 stock market index, the most prominent signals were measured during the periods of the fastest change: 1987, 2001 and 2008.

We have developed a model [1] linking the S&P 500 and its returns to the population of some characteristic age. The original model links the S&P 500 annual returns, Rp(t), to the number of nine-year-olds, N9:

Rp(t) = AdlnN9(t) + B (1)

where Rp(t) is the S&P 500 yearly return, A and B are empirical coefficients to be determined by some fitting procedure. They may change depending on the approximation used to represent N9. In the previous post on the S&P 500 returns we have approximated N9 by the number of three-year-olds, N3, six years before. Accordingly, we have obtained a prediction of the S&P return at a six year horizon, i.e. in 2010 one can foresee the returns in 2016. Relevant empirical relationship is as follows”

Rp(t+6) = 100dlnN3(t) - 0.23 (2)

Figure 1 depicts the S&P 500 returns, both actual one and that predicted by relationship (2). Both curves are coinciding in practical terms between 2008 and the middle of 2010.

In 1987 and 2001 abrupt falls in the returns were also observed. In this respect, the model also demonstrates an excellent predictive power, as Figures 2 and 3 depict. There are obvious differences between the measured S&P 500 returns. In 1987, the fall was very fast but not deep, from +0.3 to -0.1. In 2001, the returns declined gradually from +0.3 to -0.3 in 2002. In 2008, the observed curve fell from 0 to -0.5, i.e. approximately same as in 1987.

In all cases the model gives a good prediction of the timing and amplitude of the observed returns. So, the model has a good predictive power, considering that the prediction can be obtained at a nine year horizon with the birth rate used as a proxy to N9.

Therefore, one might treat our prediction of the 2011 fall as a reliable one.

During the last two weeks, the S&P 500 has been growing at a healthy pace. Currently, it exceeds the predicted level by approximately 50 to 100 points. This is a good reason to suggest that a significant force, which must eventually return the index to the trend line, has been developing in September 2010. If the growth continues into the second half of September one might expect a dramatic drop in October 2010. However, this will be just a part of the overall decrease to the level of -0.5 expected in July-August 2011.

Figure 1. The prediction of the S&P 500 annual return for the period between 2008 and 2012. We tentatively put the September's closing level at 1030.

Figure 2. The prediction of the S&P 500 annual return for the period between 1985 and 1989.

Figure 3. The prediction of the S&P 500 annual return for the period between 1998 and 2003.

References
1. Kitov, I., Kitov, O. (2010). S&P 500 returns revisited, http://ideas.repec.org/p/pra/mprapa/21733.html.

## 9/12/10

### Economics as an iterative research program

1. We do not know how it has happened …

2. The importance of the economic profession just grows …

3. Give us more time and resources …

4. Our models has been dramatically improved since the last unpredicted event …

5. We do not how it has happened again (and again) …

## 9/11/10

### On deflation, once again

Five years ago we developed an empirical model describing inflation in developed countries and published a forecast for the USA at a ten year horizon in 2006 (Kitov, 2006ab) as a linear and lagged function of labour force. For the USA the model is as follows:

DGDP(t) = 4.0dLF(t-2)/LF(t-2) – 0.03 (1)

where DGDP is the GDP deflator at time t, and LF is the level of labour force two years before, t-2. In equation (1), the slope of linear relationship is 4.0 and intercept is -0.03. With labour force constant (dLF=0), the overall price inflation in the USA would remain negative at the level of -3% per year. For inflation to be sustainable at the level of 2% (the fed’s unannounced target) the growth in labour force of 1.25% per year is required.

Eventually, the link between price inflation and labour force was successfully tested for cointegration (Kitov, Kitov, Dolinskaya, 2007) and validated using 1-D boundary elements methods (Kitov, Kitov, 2010). This links holds for other developed countries as well (Kitov, Kitov, 2010).

Figure 1 illustrates the linear and lagged relationship between inflation (GDP deflator) and the change rate of labour force in the USA for the period between 1960 and 2006. The estimates of the change rate of labour force are shifted by two years ahead in order to synchronize the predicted peaks of inflation with those actually observed in 1975 and 1984. Due to the uncertainty in the labour force (and inflation) measurements the most reliable readings correspond to the changes of the largest magnitude, as described by the BLS. Every sound model of price inflation must explain these peaks.

Figure 1. Measured inflation (GDP deflator) and that predicted from the dLF/LF (shifted 2 years ahead). An agreement is observed throughout the whole period with some short fluctuations in labour force potentially induced by the population corrections implemented by the Census Bureau in the census years.

Figure 2 presents two smoothed inflation curves – the measured and the predicted one. The smoothing was attained by a 7-year moving average, MA(7), with a one-year step. The predicted curve is shifted by 2.5 years (we used two time scales with a half-a-year shift) ahead in order to fit the inflation peak near 1978. The original (not shifted) predicted curve is also shown in order to illustrate that it is actually leading by 2.5 years.

Figure 2. MA(7) of the predicted and measured inflation. The prediction is made according to the relationship (1). The predicted curve is shifted by 2.5 years ahead.

Figure 3 displays the observed and predicted cumulative inflation curves starting from 1965. The predicted cumulative curve is obtained by a progressive summation of values from 1963 and is also shifted ahead by 2 years. There is an almost complete agreement between the cumulative curves for the whole period. The only small deviation occurred around 1993 and corresponds to a sharp drop of the rate of labour force growth as induced by the baseline working age population correction.

The predicted cumulative curve is very sensitive to free term in (1). Even the initial difference of 0.0001 results in a tangible deviation from the measured curve after 50 years. Therefore, the value of free term in (1) can be estimated with a good accuracy. With cumulative curves, one can obtain the most accurate coefficients solely on the basis of visual fit. Also, since these two cumulative curves have R2>0.999 and actually represent indexes one can replace the secular growth in the overall price as the cumulated growth in labour force two years before. In a sense, these curves are similar to conservation laws or integral equations in physics (Kitov, Kitov, 2010).
Figure 3. Comparison of the cumulative values of the observed and predicted inflation presented in Figure 1. The predicted curve starts from 1963 and is shifted by 2 years ahead. An agreement is observed with a notable change from convexity before 1980 to concavity one after 1980.

Relationship (1) can be used for a prediction at a larger time horizons using labour force projections made by various institutions. For example, the projections made by CBO (2004) and BLS (2005) undoubtedly indicate a decrease in the participation rate and a decaying growth rate of the working age population. According to these projections, staring from 2010, the annual increase in labour force will be less than 1,200,000 – the value separating inflation and deflation. Hence, the year of 2012 is likely to mark the beginning of the deflationary era in the USA (which hopefully is the global disaster the Mayans talked about) because of the two-year lag between the labour force change and inflation. Figure 4 details the prediction based on the CBO’s projection of the labour force. After peaked at 3.2% in 2007, the rate of price inflation has been at a gradual decrease, which will lead to the first red figure in 2012.

Hence, we are waiting an extended deflationary period in the USA since 2012.
Figure 4. Predicted inflation rate for the period between 2006 and 2016 according to the CBO’s (2004) labour force projection. A deflationary period starts in 2012.

References
Bureau of Labor Statistics. (2005a). Labor force projections to 2014: retiring boomers. Monthly Labor Review, November 2005.

Congressional Budget Office. (2004). CBO’s Projections of the labor force. September 2004.

Kitov, I. (2006a). Inflation, unemployment, labor force change in the USA, Working Papers 28, ECINEQ, Society for the Study of Economic Inequality,http://ideas.repec.org/p/inq/inqwps/ecineq2006-28.html

Kitov, I. (2006b). Exact prediction of inflation in the USA, MPRA Paper 2735, University Library of Munich, Germany

Kitov, I., Kitov, O., Dolinskaya, S. (2007). Inflation as a function of labor force change rate: cointegration test for the USA, MPRA Paper 2734, University Library of Munich, Germany

Kitov, I., Kitov, O. (2010). Dynamics of Unemployment and Inflation in Western Europe: Solution by the 1-D Boundary Elements Method, Journal of Applied Economic Sciences, Spiru Haret University, Faculty of Financial Management and Accounting Craiova, vol. V(2(12)_Summer), pp. 94-113.

## 9/10/10

### Does crude drive the price index of steel and iron?

This is a quarterly update.

In September 2009, we reported that the price index of crude oil had been likely evolving in sync with that of iron and steel, but with a lag of two months. In order to present both indices in a comparable form, the difference between a given index, iPPI, and the overall PPI was normalized to the PPI: (iPPI(t)-PPI(t))/PPI(t). The normalized differences represent the evolution of the rate of deviation from the PPI over years.

Figure 1 depicts the corresponding time histories of the normalized deviations from the PPI, including the most recent period since June 2010. Simple visual inspection reveals the following feature: the (normalized deviation from the PPI of the) index of iron and steel lags by two months behind the (normalized) index of crude oil.

Figure 1. The deviation of the iron and steel price index and the index of crude oil from the PPI, normalized to the PPI.

In order to reduce both deviations to the same scale we additionally normalized the curves in Figure 1 to their peak values between 2005 and 2009:

(iPPI(t)-PPI(t))/[PPI(t)*max{iPPI-PPI)}]

This scaling allows a direct comparison of corresponding shapes. In Figure 2, we display the normalized index of iron and steel shifted by two months ahead to synchronize its peak with that observed in the normalized index for crude petroleum. (The period between May and July 2010 is included.) The scaled index of crude demonstrates just minor discrepancies from the index of iron and steel in the overall shape and timing of the peak and trough. Simple smoothing with MA(3) makes the curves resemblance even better. As an invaluable benefit of the resemblance, one can use the two-month lag to predict the future of the iron and steel price index.
Figure 2. Deviation of the iron and steel price index from the PPI, normalized to the PPI and the peak value after 2005 as compared to the deviations of the index for crude petroleum normalized in the same way. The normalized index for iron and steel is shifted two months ahead.

Conclusion
Between 2006 and 2010, the deviation of the price index of iron and steel from the PPI in the USA repeats the trajectory of the deviation of the index of crude petroleum (domestic production) with a two-month lag. Therefore, the prediction of iron and steel price for at this horizon is a straightforward one. It is likely that in the fourth quarter of 2010 the index of iron and steel will be decreasing following the observed fall in the index for crude petroleum.

References
Kitov, I., Kitov, O., (2009). Sustainable trends in producer price indices, Journal of Applied Research in Finance, Spiru Haret University, Faculty of Financial Management and Accounting Craiova, vol. I(1(1)_ Summ), pp. 43-51

### PPI of copper ores and grains

Three months ago we revisited our prediction on the evolution of the PPI of copper and grain, which had been made in 2009. The original prediction limited the level of the copper PPI in 2010:

Therefore, copper price will likely not be growing to its peak in April 2008 (491.7), but will likely return to heights around 350.

Three months ago we suggested that:

In the short-run, the index for copper will NOT be growing too long, at least NOT till the end of 2010.

Figure 1 compares the prediction and actual behavior for the producer price index of copper ores relative to the overall PPI. All in all, the prediction was right: by the end of 20009 the price index of copper has reached the level of 350 (375) and even higher in the beginning of 2010 (443 in April). However, it has not reached the 2008 level and started to fall in the second half of 2010. It is very likely that the fall will continue, potentially with a higher volatility, in 2010 and will be stretched into 2011. At a two to four year horizon, the price index for copper ores should return to the pre-2008 level.

Figure 1. Evolution of the price index of copper ores relative to the PPI.

The price index for grains has been following our predictions as well. In 2009 we wrote:

It is instructive to compare two major spikes in the grains index in 1996 and 2008 relative to the PPI. In order to avoid comparing absolute values, which undergo secular growth, the evolution of the difference between the PPI and the price index of grains normalized to the PPI. Figure 3 presents the normalized curves. The left panel shows that the spike in the grains PPI in July 1996 is similar in relative terms to that observed in 2008. The right panel tests this hypothesis: the spikes are synchronized - for the black line is shifted forward by 142 months. From this comparison, it is likely that decline in the grains index relative to the PPI will extend into the 2010s.

Three months ago we confirmed the prediction that the index of grains would follow the path observed in 1996:

The index for grains will continue its decline relative to the PPI. As a consequence, one can expect that the index for food will be also decreasing and this decline will stretch into the 2011

Figure 2 illustrates the accuracy of our prediction of the index of grains. The index has been falling relative to the PPI and the trajectory actually repeats that observed 142 months before. We expect the normalized difference to follow up the once observed recovery path. It is interesting that this time the through was not as deep as in 1996. Supposedly, there are more efficient mechanisms counteracting the growth in the price of grains when were in 1996. Judging from Figure 2, the index of grain will not be growing during the next several years.

Figure 2. Evolution of the difference between the PPI and the price index of grains normalized to the PPI. Upper panel: Comparison of the current curve to that observed 142 months ago. Lower panel: Same as in the upper panel; the most recent period.

Short term prediction
In the short-run, the index for copper will likely be falling in 2010. The index for grains will continue its slight decline relative to the PPI.

1. Kitov, I., Kitov, O., (2008). Long-Term Linear Trends In Consumer Price Indices, Journal of Applied Economic Sciences, Spiru Haret University, Faculty of Financial Management and Accounting Craiova, vol. 3(2(4)_Summ), pp. 101-112.

2. Kitov, I., (2009). Apples and oranges: relative growth rate of consumer price indices, MPRA Paper 13587, University Library of Munich, Germany.

3. Kitov, I., Kitov, O., (2009). A fair price for motor fuel in the United States, MPRA Paper 15039, University Library of Munich, Germany,

4. Kitov, I., Kitov, O., (2009). Sustainable trends in producer price indices, Journal of Applied Research in Finance, v. 1, (in press)

5. Kitov, I., Kitov, O., (2009). PPI of durable and nondurable goods: 1985-2016, MPRA Paper 15874, University Library of Munich, Germany

6. Kitov, I., (2009). Predicting gold ores price, MPRA Paper 15873, University Library of Munich, Germany

7. Kitov, I., (2009). Predicting the price index for jewelry and jewelry products: 2009-2016, MPRA Paper 15875, University Library of Munich, Germany