Blog lost: Inflation in the USA. Successor blog: Economics as Classical Mechanics

By mistake, I have removed my two-year-old blog “Inflation in the USA” with about 200 posts. I can not completely recover it. Therefore, this new blog is the successor, and inherits the best posts. I'll keep presenting new results on the evolution of real GDP, productivity, labor force participation, inflation, and unemployment world-wide. The prediction of S&P 500 returns is my major topic as well.

As of now, I will also pay more attention to the presentation of my book on economics with a tentative title ”Economics as Classical Mechanics” or “Mechanomics”. I’ll publish paragraphs and chapters, when ready. As one may see, the title of this blog is relevant to this task.

The purpose of this blog and the book is to present quantiative arguments supporting the predictability of economic evolution as expressed in the macro-variables listed above. Economics is a hard science in terms of the existence of robust links between major measured macroeconomic variabels. It this sense, economics is equivalent to the classical mechanics.

At the same time, I am highly interested in applied research like share and commodity pricing.
Eventually, any post in this blog adds to a working paper or article. So, this blog is a notebook of quantitative research.

Unemployment in Japan above 6% in August

Since the beginning of 2009, unemployment rate in Japan has been on rise. The Statistical Bureau of Japan has just announced a severe increase in (seasonally adjusted) unemployment rate up to 5.2% in May. There was only 4.1% in December 2008.

We have predicted that effect in our early paper [1] and confirmed the prediction in the detailed study of unemployment in Japan [2]. The most recent estimate of the linear link between unemployment rate and the change in labor force level gave the following relationship:

UE(t)= -1.5dLF(t)/LF(t) +0.045 (1)

where UE is the unemployment rate at time t, LF is the level of labor force at the same time. Thus, there is no time delay between the change in the LF and UE, as it observed in many developed countries [3,4].

As with any quantitative prediction of future time series, one can check and validate the underlying relationship using new data. Figure 1 compares the measured unemployment rate in Japan to that predicted from (1). Relevant labor force estimates are also retrieved from the Statistical Bureau's table.

Between 1999 and 2008, the prediction is excellent considering the accuracy of both unemployment and labor force measurements. The deviation between the curves observed in 2008 is the largest. However, it does not influence the accuracy of the 2009 prediction, which is definitely outstanding in terms of dynamic resolution. The dramatic increase in unemployment rate is well predicted by relevant fall in the level of labor force; the slope in (1) is negative. Because of the absence of time delay between these two variables, it is difficult to distinguish between action and reaction. Our previous investigations indicate that the change in labor force in the primary source of the change in unemployment and inflation [3,4].

Figure 1 demonstrates that the rise in unemployment is not finished yet and it may reach 6% in August 2009. Since the labor force estimates are contemporary to the unemployment one, we can not stretch them into 2010. However, labor force projections for Japan show that the unemployment will approach 5% in the long run, as depicted in Figure 2 borrowed from [2].

Figure 1. Comparison of observed and predicted unemployment rate in Japan.

Figure 2. Unemployment rate projection for Japan between 2010 and 2050 [2].

References

[1] Kitov, I., (2006). The Japanese economy, MPRA Paper 2737, University Library of Munich, Germany, http://ideas.repec.org/p/pra/mprapa/2737.html
http://mpra.ub.uni-muenchen.de/2737/01/MPRA_paper_2737.pdf

[2] Kitov, I., (2007). Exact prediction of inflation and unemployment in Japan, MPRA Paper 5464, University Library of Munich, Germany,
http://ideas.repec.org/p/pra/mprapa/5464.htmlhttp://mpra.ub.uni-muenchen.de/5464/01/MPRA_paper_5464.pdf

[3] Kitov, I., (2007). Exact prediction of inflation and unemployment in Germany, MPRA Paper 5088, University Library of Munich, Germany,
http://ideas.repec.org/p/pra/mprapa/5088.html
http://mpra.ub.uni-muenchen.de/5088/01/MPRA_paper_5088.pdf

[4] Kitov, I., Kitov, O., (2009). Unemployment and inflation in Western Europe: solution by the boundary element method, MPRA Paper 14341, University Library of Munich, Germany, http://mpra.ub.uni-muenchen.de/14341/01/MPRA_paper_14341.pdf

The CPI of transportation revisited

We continue revising our predictions of the CPI subcategories from 2007 [1]. The past twenty months have revealed high turbulence in the behavior of the index for transportation. Here we are going to compare our predictions with actual observations and revise the predictions where appropriate. We have started with the index of food and continue with the index for transportation.
Below is an excerpt from the paper, as devoted to transportation:

“The difference for the transportation index had a longer period of positive slope – between 1980 and 2004, as Figure 11 demonstrates. During this period the difference was evolving at a rate of 1.5 units per year and reached the level of 30 units of index. Currently, a turning period is likely observed and a negative slope is developing. The current period is accompanied by an elevated volatility. The slope for the future linear trend, which is estimated as -1.25 units per year in Figure 12, will be possibly changed in near future but will define the duration of the recovery period for the transportation index. In any case, the prices for goods and services related to the index for transportation, as it defined by the BLS, are very likely to be growing faster than the headline CPI.

Figure 11. The difference between the CPI and the transportation index between 1960 and 2007. Notice two clear periods of practically linear trend: between 1960 and 1980; between 1980 and 2000. Currently, a period of turning to a new trend is observed – the transportation index will be growing faster than the CPI. This turn is accompanied by very high volatility.

Figure 12. Same as in Figure 11 for the period after 2002. The transportation index likely started to grow faster than the CPI. New linear trend has not finally developed and more volatility might be expected in the housing expenditure category. “


Instead of the difference between the (seasonally adjusted) headline CPI and the index for transportation we inspect the deviation of the transportation index from the core CPI. In 2008, after a short period of increase induced by the spike in oil price, the index for transportation has been decreasing at a very high rate relative to the core CPI, as shown in Figure 1. This was a dramatic drop in the index from 2007 in July to 167 in December. Apparently, it was also caused by the fall in oil price from $149 to $38. Goods and services related to transportation easily lost their pricing power. So, one can assume that that index was driven by an external force.
Since January 2009, the prices index for transportation has been oscillating around the level 170 despite crude oil price almost doubled. Such behavior is difficult to explain but it seems that transportation loses its bounds to oil price. So, it is likely (e.g. this is our working hypothesis) that the difference will eventually return to the old trend, as shown in Figure 2. This process should be accompanied by an intensive growth in the index itself. It should jump in the near future to the level around 200. In the long-run, the difference will reach its turning point somewhere between 2015 and 2020, if our assumption on the presence of long-term sustainable trends is applicable to the index of transportation. If the transportation index returns to the trend, it will be the best validation of our approach.

Figure 1. The difference between core CPI and the index for transportation between 1960 and 2009.

Figure 2. Comparison of the trend predicted in 2007 and that in 2009. Current change in the index for food shifts the new trend towards the old one.
Conclusion
There are several preliminary conclusions about the past and the future of the index of transportation can be derived from Figure 2.
The consumer price index for transportation is sensitive to the changes in the index for energy and crude oil price, but likely the bounds loosen with time.
Since 2008, the index for transportation has been characterized by very high volatility, which currently makes any further prediction unreliable.
The difference between the core CPI and the index for transportation will be likely decreasing in absolute terms by the end of 2009 with a possible stretch into 2010.
The index of transportation itself should increase by ~30 units in the near future.
The difference should return to the long-term trend with the next turning point between 2015 and 2020.

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. 3(2(4)_Summ), pp. 101-112.

The CPI of food revisited

In 2007, we predicted the evolution of several consumer price indices relative to core CPI in the USA [1]. The past twenty months have revealed high turbulence in the behavior of such major expenditure subcategories as energy, food, housing, etc. We are going to compare our predictions with actual observations and revise the predictions where appropriate. Here we start with the index of food.
Below is an excerpt from the paper, as devoted to food:

“Figure 7 displays the difference between the core CPI and the index for food for the period after 1960. This curve differs from that in Figure 5. The first large change in the difference occurred in 1973 (not in 1979 as for energy) and lasted only 7 years. Around 1980, the difference started to grow from -7.0 to 13.0 in 1996. Between 1996 and 2003, the difference was effectively constant at the level of ~13.5 units of price index, i.e. a lengthy flat segment was observed. After 2003, the difference has been decreasing at a rate of 1.2 units per year, as Figure 8 demonstrates.
Overall, the difference between the core CPI and the food index was always lower than that between the energy index and the core CPI. The largest difference was only around 14 units. Since 2003, the food price index has been slowly catching up the core CPI. Extrapolating the current linear trend one can estimate the intercept point when the food price index will reach the core CPI. According to Figure 8, this will happen in 2014. Such a behavior differs from that observed for the energy index in terms of timing and amplitude, but the overall behavior distinguishing periods of linear growth and bifurcation is very similar. Therefore, principal mechanisms behind the evolution of the food price index are similar to those behind the energy index. They are likely not related to the changes in supply pressure induced by good crops and draughts. These mechanisms have to be a part of economic system itself and should be related to relationships between economic agent not to production of goods and services.

Figure 7. The difference between the core CPI and the index for food between 1960 and 2007. There are three periods of linear trend and two turning periods. The most recent period of linear trend started in 2003.

Figure 8. The difference between the core CPI and the food index between 2002 and 2007. The current period of linear trend will be likely finished in 2014. Since 2003, the food price index has been slowly catching up the core CPI. “


Since 2007, the index for food has been rising at an elevated rate compared to that predicted by the long-term-trend in Figure 8 in the excerpt. Figure 1 displays the difference between the (seasonally adjusted) core CPI and the index for food (beverages not included). A remarkable rally in food prices forced the index for food to grow faster than predicted and the deviation from the trend predicted in 2007 reached ~7 units in 2008. This behavior was likely related to the outstanding rally in oil price finished in July 2008. Correspondingly, almost all prices were driven up. After July 2008, the same prices have been declining at a higher rate sharing the faith of crude oil price. Accordingly, from January 2009, the index for food started to decline in absolute terms at its returning path to the old trend shown by pink line in Figure 1. The current trend, as shown by black line, is far enough from the old one, but the difference has a good speed approaching the pink line.
The pink line intersects the zero line around 2014. The new trend hits the zero line in 2010. Because of the current recovery to the old trend, actual interception should happen somewhere between 2011 and 2014. This interception point will likely to be the turning point to a new trend. Currently, we observe a similar process for the price index of energy. The discrepancy between the turning points for these expenditure categories is of fundamental importance – there are different sets of economic forces behind them.


Figure 1. Comparison of the trend predicted in 2007 and that in 2009. Current change in the index for food shifts the new trend towards the old one.

Conclusion
There are several conclusions about the past and the future of the index for food can be derived from Figure 1.

• The consumer price index for food is sensitive to the changes in the index for energy and crude oil price.
• The deviation from the well established trend in the difference between the core CPI and the index for food observed between 2002 and 2007 has been driven by the outstanding rally.
• The difference between the core CPI and the index for food will be likely increasing in absolute terms by the end of 2009 with a possible stretch into 2010.
• The index of food itself may be decreasing in absolute terms.
• The new trend for the index for food will start emerging somewhere between 2011 and 2014. Since the turn to the new trend, the index for food will start to lose its ground relative to goods and services comprising the core CPI. In other word, food will become cheaper in relative terms.

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. 3(2(4)_Summ), pp. 101-112.

PPI of copper ores and grains

Lately, we have demonstrated that the evolution of various components of CPI and PPI in the United States is not a random process but rather a predetermined one with long-term sustainable trends [1-4]. Using these trends, one can predict consumer and producer price indices for various goods, services and commodities [5-7]. Moreover, share prices for selected S&P 500 companies are also well described in the past by the differences in the PPI and CPI [8,9]. The near future will test the predictive power of our model.
In [4-7], we presented the evolution many commodities with varying weight in the PPI. But there are many more commodities of interest for producers, consumers, and investors. Here we compare the indices for copper and grain. The evolution of the producer price indices of these two commodities is independent, but both give a good example of the absence of clear sustainable trends. In other words, not every commodity price is predictable, as mentioned in [4].
Figure 1 displays the PPI and the index for copper ores since 1988. The difference of these two indices has a remarkable history; no big change between 1988 and 200, and then a sudden peak in the copper index. The peak survived during three years before 2008, and then the index dropped by 300 units back to the PPI level. Despite the early start in 2005 seems oil independent, the most recent fall looks to be driven by oil price and the overall economic slowdown. In 2009, one can observe a slight increase in the cooper index likely associated with the rise in oil price. In the long-run, oil price should decline to the level of $25 in 2016. Therefore, copper price will likely not be growing to its peak in April 2008 (491.7). In any case, our model based on the presence of sustainable trends in the difference between the PPI and individual PPI is not applicable to copper ores.

Figure 1. Evolution of the price index of copper ores and the PPI.

The producer price index for grains presents another difficult case. Figure 2 depicts the PPI and the index, and their difference between 1960 and 2009. There is no sustainable trend in the difference as well. Between 1974 and 2005, the difference demonstrated an overall growth with several spikes, the strongest one in 1996. The presence of a long-term positive trend in the difference is completely due to the growth in the PPI because the index for grains fluctuates around a constant line. Lately, the grains index suffered the biggest rise and fall in absolute terms. The main increase started earlier 2007 and stretched into 2008, with the peak in June 2008. Volatility during the past two years was so high that it is difficult to predict the next move of the price index of grains. Seemingly, it has been repeating the trajectory of the index for crude oil in 2008 and 2009. If it is the case, one can expect that the index for grains will continue to oscillate around the constant level of ~100.

Figure 2. Evolution of the price index of grains and the PPI.

Conclusion
The producer price index for copper and that of grains both demonstrate unpredictable behavior with unclear future. This observation only emphasizes the importance of sustainable trends observed for other commodities. In the US economy, as in many natural systems, there exist trend components, oscillating components, and random components.

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. 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
8. Kitov, I., Kitov, O., (2009). Predicting share price of energy companies: June-September 2009, MPRA Paper 15863, University Library of Munich, Germany
9. Kitov, I., Kitov, O., (2009). Modelling selected S&P 500 share prices, MPRA Paper 15862, University Library of Munich, Germany

Crude oil will reach $100 by December 2009

Introduction
The change in producer and consumer prices has been always a concern for all stock market participants. Lately, we have demonstrated that the evolution of various components of CPI and PPI in the United States is not a random process but rather a predetermined one with long-term sustainable trends [1-4]. Using these trends, one can predict consumer and producer price indices for various goods, services and commodities [5-7]. Moreover, share prices for selected S&P 500 companies are also well described in the past by the differences in the PPI and CPI [8,9]. The near future will test the predictive power of our model.
In [1,3], we gave a prediction of the index for energy as a proxy of crude petroleum price. In [5-7], we developed a technique to describe the evolution of producer prices for gold, jewelry, durable and nondurable goods. So, there was no direct prediction of crude oil price. This paper fills this gap.

1. The model
The model derived in [4] implies that the difference between the headline PPI, PPI, and the index for crude petroleum, cpPPI, can be described by a linear time function over time intervals of several years:
PPI(t) – cpPPI(t) = A + Bt (1)
where A and B are empirical constants, and t is the elapsed time. Therefore, the “distance” between the PPI and the studied index is a linear function of time, with a positive or negative slope B. Figure 1 displays this difference between 1985 and 2009. There are two distinct periods of linear dependence on time: from 1988 to 2000, from 2001 to 2008. There was one transition period between 2000 and 2001, where the trends undergo changes. This turning point was characterized by an elevated volatility. Since 2008, the difference has been also passing a turning point with very high volatility caused by the uncertainty in the characteristics of the following trend.
Figure 1 presents quantitative parameters of the linear trends. Between 1988 and 2000, the difference was growing at a rate of B=+2.0 units per year. Between 2001 and 2008, the difference underwent a rapid fall at a rate of B=-21 units per year. Both trends are reliable ones with a high goodness-of-fit.
A fundamental feature of the difference consists in the fact that all deviations from the trends were only short-term ones. This implies that the current or future deviations from the new trend, which has been under development since 2008, must be rapidly compensated. This feature allows a short-term (months) price prediction.

Figure 1. Illustration of linear trends in the difference between the headline PPI and the producer price index for crude petroleum (domestic production) in the U.S. There are two quasi-linear segments with a turning point near 2000. Currently, the difference passes second transition period. Two linear trends with relevant linear regression lines and corresponding slopes are also shown.

2. Price prediction
Having relationship (1) which describes the evolution of the studied difference one can easily predict its short- and long-term evolution. Figure 2 depicts the time history of the PPI and the index for crude petroleum. The PPI curve is smoother because it represents a weighted average of all commodities. The curve for crude petroleum has several peaks, but that in 2008 is by far the largest in absolute terms. Our purpose is to continue both curves in line with their long-term and short-term behavior in the past.
Since 2008, the new linear trend has been emerging. At this point, it is difficult to accurately estimate the rate of future growth. A naive assumption is that the following period will be a “mirror reflection” of the previous one. Therefore, the new slope should be the same as the old one but with opposite sign. Figure 1 shows the new trend by solid green line – the difference will grow from -55 in 2008 to +80 in 2016. In Figure 2, we split the difference into its component: the PPI will be growing with an annual increment of +2 units [10-12] and the index of crude petroleum will be decreasing by 19 units per year. This makes the slope B=+21. Therefore, the price for crude oil will be falling between 2010 and 2016. The start level is 190 units for both indices. Similar prediction has been already done for the index of motor fuel [3].

Figure 2. Evolution of the price index for crude petroleum and the PPI.

The difference curve in Figure 1 is not a straight line, however. Both observed trends are the gravity lines for the actual curve, but deviations from the trends have large amplitudes. Good news is that they are also back to the trends. This property allows a short-term prediction. Figure 3 displays the past five years. The largest ever deviation from the trend started in the beginning of 2008. One would guess that the uncertainty of the future behavior of crude price caused an euphoria among market players …, which quickly mutated into panic in the end of 2008. Accordingly, the price high-rocketed to ~$150 and then did not returned to the new trend but dropped to ~$40. In physics, such process would be called “dynamic overshoot”. One can also use the term rebound.
What can we say about the near future of the difference after May 2009, i.e. the last point currently available? A reasonable assumption of the next move in the difference is shows by red diamond – the curve will follow its natural motion down to the new trend and, after intersection of the red line, the difference will continue its way down. What will be the rate of rise in the index of crude petroleum? This is a big question. We assumed +20 units per month till the end of 2009, i.e. the index will grow from 157 in May to 297 in December. It is worth noting, that there is a trade-off between the rate of growth and the duration of the growth – if the rate is higher the duration is shorter. One can draw own line between June and December.

Figure 3. The difference between the PPI and the index for crude petroleum. Red diamonds predict the evolution of the difference between June and December 2009. Total increase in the difference is -140 units of index Solid red line represents the “mirror” trend for that between 2002 and 2008, which is shown by solid black line.

Finally, what will be crude petroleum price by the end of 2009? One can easily convert price indices into real prices. We used a conversion factor of 2.65, which corresponds to $145 per barrel in July 2008 and the index of 384.3. The price for June 2009 is $66.8 with the index of 177. Crude price in December is at $112. This is our best prediction using the assumption of the index growth. The price might be reached earlier or later depending of rate of growth. The absolute value of $112 also can also be higher or lower depending on the amplitude of the overshoot in the second part of 2009.

Figure 4. The evolution of crude petroleum (domestuic production) price. Between June and December 2009 the price will rise from $66 to $112 per barrel.


Conclusion
The price index for crude petroleum (domestic production) in the USA should grow by ~45% between June and December 2009 – from $66 to $112. Our simple model suggests a constant price increment over months, but actual monthly growth might not be even. In any case, the total price increase in 2009 should compensate the fall in 2008 and demonstrate some price “overshoot” relative to the trend line, i.e. the price will rise above the trend.
It is likely that the price index for crude petroleum will be decreasing in absolute terms approaching the level of 60 units in 2016. This corresponds to $22 per barrel.

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. 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
8. Kitov, I., Kitov, O., (2009). Predicting share price of energy companies: June-September 2009, MPRA Paper 15863, University Library of Munich, Germany
9. Kitov, I., Kitov, O., (2009). Modelling selected S&P 500 share prices, MPRA Paper 15862, University Library of Munich, Germany
10. Kitov, I. (2006). Inflation, unemployment, labor force change in the USA, Working Papers 28, ECINEQ, Society for the Study of Economic Inequality
11. Kitov, I., (2006). Exact prediction of inflation in the USA, MPRA Paper 2735, University Library of Munich, Germany
12. 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

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Gold Declines as Stronger Dollar, Weaker Crude Oil Curb Demand