PPI v. core PPI

Six years ago we first reported on the presence of sustainable trends in the difference between various components of PPI [1]. Figure 1 illustrates the concept by highlighting two quasi-linear trends in the difference between the overall PPI and the core PPI, i.e. the PPI less food and energy. Both indices are not seasonally adjusted ones and represent finished goods (http://www.bls.gov/data/). We predicted that the trend observed between 2001 and 2008 had to come to end. A new trend had to develop and to define the prices of commodities in the 2010s. This new trend was expected to have a positive slope, i.e. the price indices of energy and food should grow at a lower rate than those for other commodities.  

Figure 1 displays the time history of the difference and two slopes of the relevant trends. Between 1980 and 2000, the difference was growing at a rate of 0.79 per year. Between 2001 and 2008, the difference fell at a rate of 3.4 units of index per year. Since 2008, this trend, which was reigning between 2001 and 2008, started to fade away and a new trend have been emerging. This period is characterized by very high volatility. The fall in the difference observed in 2008 was followed by a positive spike in 2009 and again by a fall in 2010. In 2011, the difference stabilized and has been following the expected trend ever since. This is the pattern we accurately foresaw in 2008.
The concept of sustainable trends allows predicting the future evolution of the difference. The new trend is likely defined. Figure 2 depicts the period after 2000 and highlights the new trend with a slope 0.77 units of index per year. This slope is the same as between 1980 and 2000. Initially we put forward two naïve assumptions that the new trend has to repeat the previous one with a positive sign or the one between 1980 and 2000. The latter hypothesis is likely right. 

Figure 1. The difference between the core PPI and the overall PPI between 1974 and 2013. There are two distinct period of quasi-linear trends: 1980-2000 and 2001-2008.

Figure 2. A new sustainable trend has been emerging since 2011. Green line - an assumption on the new trend.


On further fall in copper price

Since 2008, we have been reporting 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, 2]. Using these trends, one can predict consumer and producer price indices for various goods, services and commodities.  For example, in [3, 4], we presented the evolution many goods and services with varying weights in the CPI. There are more goods, services, and commodities of interest for producers, consumers, and investors. Here we revisit the index for copper ores. This is an example showing that some commodity prices are not well predictable.

Figure 1 displays the difference between PPI and the index for copper ores since 1988. This difference has a remarkable history: no big change between 1988 and 2003, and then a sudden surge in the copper index started. The peak was reached in the middle of 2006. It survived before the second quarter of 2008. Then the copper index dropped by almost 300 units back to the PPI level. In 2009, the PPI of copper increased above 500.  One may consider these changes as associated with the rise-fall cycles in oil price, but there is no one-to-one correspondence.

We have to admit that there is no sustainable trend in the copper index and the future of the copper ores index cannot be predicted in the long run. Currently, the difference is right in the middle between the previous trough and zero line. Moreover, it has reached the level of the previous local peak in 2007 (see Figure 2 for relative prices). Therefore, the PPI of copper may go any direction in 2014. I would refrain from buying/selling this commodity before the next clear sign of the future evolution. Considering the overall fall in commodities (oil, various metals, grains, etc.), I would not exclude further fall in the PPI of copper.

Figure 1. Evolution of the price index of copper ores relative to the PPI. 
Figure 2. Evolution of the difference of the PPI and price index of copper ores normalized to the PPI. Both troughs have the same depth.  

Food is getting cheaper

This is an annual update. We continue reporting on and predicting the evolution of the difference between the core consumer price index (CPI) and the index for food (less beverages).  Previously, we confirmed in many posts (see this blog) and papers [1, 2] that this difference had been following a long-term negative and linear (time) trend since 2001.  Originally, we predicted a turn to a positive trend in 2014. Two years ago, we expected the turn to a positive trend in 2012. Currently, we have new estimates of the core and food CPI through October 2013and can re-estimate the duration of the negative trend and its bottom value. For an investor dealing with commodities, the index of food, which continues to grow at a rate higher than the core CPI, is an important reference for any action. Food price affects not only economic but also social and political processes.

Figure 1 depict the most recent period. In 2008, when we first addressed the issue of sustainable trends in CPIs, the trend line was much steeper than now and intersected the zero line in 2014.  This was our initial estimate of the turning point for the negative trend. The zero line was considered as a natural level of resistance.  In the beginning of 2009, the difference reached the bottom and turned to a positive one, although not for long. The growth in food prices restarted in 2010. In the end of 2011, the difference had a short stop which we likely misinterpreted as a manifestation of the transition to a positive trend. Since October 2011, the difference has not been changing much with just a slight positive trend. This segment might manifest the major turn to the overall food price fall.

There are three possibilities of the future evolution. Firstly, we consider the probability of the turning point in 2013-2014 to be high.  Secondly, it is not excluded that the difference may suffer a further slight fall before it reaches its absolute historical minimum observed in 1979. Figure 2 illustrates this assumption and implies that with the current values at the level -3.0 and the bottom was at -8.5. Thirdly, the bottom value may be expressed in relative values. Figure 3 displays the difference between the core and food CPI normalized to the core CPI. In relative terms, the minimum was in 1974 and much deeper than in absolute terms. Falling along the current trend the normalized difference will reach the bottom only in the 2020s. This is the worst case scenario involving a significant rise in food prices through the 2010s. 

The first version is supported by the evolution of the producer price of grains in Figure 4. This price has been falling relative to the PPI since August 2013. The price of grain should affect the overall consumer price of food, likely with some delay. This facilitates the realization of the first scenario. Then food will be getting cheaper relative to the headline CPI.
Figure 1. The difference between the core CPI and the price index of food since 2002.  
Figure 2. The difference between the core CPI and the price index of food between 1960 and October 2013.
Figure 3. The difference between the core CPI and the price index of food normalized to the core CPI.  
Figure 4. The difference between the PPI and PPI of grains normalized to the PPI. The PPI of grains rapidly falls relative to the PPI since August 2012.


Price of steel and iron will be declining another three years

Five months ago we revisited the previously predicted fall in the producer price index of steel and iron in the first half of 2013 and formulated the hypothesis on the evolution in 2013-2016: “We foresee that the difference will be growing fluctuating around the green line till 2016. The price of iron and steel will be declining further before the difference reach ~10 to 20.  It is time to revisit our prediction.  

Originally, we reported on the difference between the overall PPI and the PPI of steel and iron in 2008. Then we revisited the difference in 2010, February 2012, December 2012, and August 2013. We predicted the index of steel and iron to return to the long term trend, which express a higher rate of growth of the producer price index than that of steel and iron. Our general approach is based on the presence of long-term sustainable (linear and nonlinear) trends in the evolution of the CPI and PPI in the United States [1, 2]. The difference between various components of these indices is not a random one but is rather a predetermined process. Using these trends, one can predict consumer and producer price indices for select goods, services and commodities.  

Figure 1 displays the difference between the PPI and the index for iron and steel (BLS code 101). The difference is characterized by the presence of a sharp decline between 2001 and 2008. Between 1985 and 2000, the curve fluctuates around the zero line, i.e. there was no linear trend in the absolute difference. In 2008, our main assumption was that the negative trend observed before 2008 should start transforming, after a short period of large fluctuations, into a positive trend after 2010. In Figure 1, the (originally expected) new trend is shown by green line. This trend suggests that the PPI grows faster than the index of steel and iron by approximately 2 units of index per year.  

Figure 2 demonstrates the most recent period and confirms that our prediction for 2013 was correct – the difference fluctuates around the green line. Therefore: 

We confirm our early prediction that the price of iron and steel will be falling through 2016  before the discussed difference reach ~10 to 20.  Investments in iron and steel related assets are likely not profitable.  

Figure 1. The difference of the PPI and the index of steel and iron updated for the period between November 2012 and October 2013. The green line was first introduced in 2008.

Figure 2. Same as in Figure 1 for the period between January 2005 and October 2013. Green line predicts the evolution of the difference after 2008.


Price of nonferrous metals will decline into 2015. Aluminium price will likely reach its bottom in 2015.

This is a regular revision. We have been following the evolution of several price indices of metals since 2008. Our general approach is based on the presence of long-term sustainable (linear and nonlinear) trends in the evolution of the CPI and PPI in the United States [1, 2]. The difference between various components of these indices is not a random one but is rather a predetermined process. Using these trends, one can predict consumer and producer price indices for select goods, services and commodities. 
In this post, we revisit the trends in the PPI of nonferrous metals. Originally, we reported on this item in 2008 and then revisited in 2010 and February 2012. The index for non-ferrous metals (102) shows an example of the absence of sustainable trends in the difference (see Figure 1). The curve is rather a comb with teeth of varying width. Although varying, the distance between consecutive troughs is several years at least. 

We predicted that the index of nonferrous metals had to fluctuate with large amplitude around the PPI and grew at a lower rate than PPI during 2012 and 2013:  Considering the observation that the rate of growth was approximately 3 points per month since February 2012 one may expect the level of -10 in approximately 10 to 12 months, i.e. in September 2013.” 

In reality, this difference was at -36.6 in October 2013. There was an overall fall during 2013. Therefore, the difference will follow the schedule (linear trend) marked by green line and likely extend into 2015.  

The producer price index of aluminum base scrap has to follow the same trend, as Figure 2 shows. However, the liner trend will intersect the zero line in 2015 and the potential for further fall might be exhausted.  

The price of aluminum will be decreasing in 2014 but likely reach its bottom in 2015. 

Figure 1.  The evolution of the difference between the PPI and the index of nonferrous metals from 1985 and October 2013. There are no linear trends in the difference, but its behavior demonstrates a clear periodic structure with relatively deep but short troughs, which reflect the fast growth in the PPI for nonferrous metals.

Figure 2.  The evolution of the difference between the PPI and the index of aluminum base scrap from 1985 and October 2013.



Consumer price inflation, headline and core

We have been routinely reporting on the difference between the headline and core CPI since 2008. Figure 1 illustrates our general finding that this deference can be well approximated be a set of linear trends. The last trend likely finished in 2009. That’s why we expected a new trend to evolve since 2011 into the late 2010s.

The U.S. Bureau of Labor Statistics has reported the estimates of various consumer price indices for October 2013. Figure 2 shows the predicted trend and the actual difference since 2002. The difference has been fluctuating around zero between in 2009 and 2011 and then showed a turn to the predicted trend.  Essentially, the zero difference suggests that the core and headline CPI are practically equal and evolve at the same monthly rate, i.e. the joint price index of energy and food has been following the price index of all other good and services (the core CPI) one-to-one.

Currently, the price index of energy slowly falls together with oil price. We expect them to fall deeper and thus the headline CPI to decelerate a bit together with energy. If the core CPI will retain its current cohesion with the headline CPI, we will have a period of very low inflation in all goods and services less energy and food. 

Figure 1. Two trends in the difference between the healine and core CPI.

Figure 2. The evolution of the difference between the core and headline CPI since 2002.


The Fed does not affect unemployment


B. Bernanke gave a talk yesterday. We presented quite a few arguments (e.g. here) why the Fed cannot control inflation with all instruments like rates and QEs. Here we present a simple case showing that the Fed does not affect the rate of unemployment as well. Comparing three recent trajectories of unemployment fall after it peaked. The dry residual is that nothing has changed with the negligible overnight rate and the money poured into the system.  Literally, there is no reaction at all.  

The rate of unemployment, u, was very high (10%) in 2009. It was recognized by the Fed and economic community that the fall in this rate is too slow historically and the Fed has to take some immediate measures to expedite the reduction to, say, 6.5%. Such measures were taken.

Figure 1 shows the evolution of u since 1980. There were two major peaks in 1982 (10.8%) and 1992 (7.8%). (All rates are seasonally adjusted.) Let’s compare the fall trajectories. When the troughs preceding the peaks are synchronized all three descending curves look very similar. This observation says that the current fall in u is not different from the previous. With or without QE, unemployment falls at the same rate.

It also tells us a fairy story about the near future. This rate will fall into the second half of the 2010s. Meanwhile, it may fall to 6.0% in 2013 or in the first half of 2014.


Figure 1. The rate of unemployment in the US 

Figure 2.  Two previous major peaks synchronized with the most recent. The length of fall is approximately 7 years.


Did we predict low inflation in the US seven years ago?

Todd Clark and Saeed Zaman from the Federal Reserve Bank of Cleveland wrote a few  days ago:
"Many observers have been surprised by the decline in consumer price inflation that has occurred since early 2012. "

Seven years ago we first presented a model predicting the rate of  US inflation at a ten year horizon. The paper was published by the Society for the Study of Economic Inequality (ECINEQ) and ended by the following statement: "The current period of disinflation will probably transform into deflation starting 2010-2012."
Since the model is based of labor force projections, the accuracy of inflation prediction critically depends on the precision of labor force estimates. Another paper introduces a corrected labor force estimate and  states that the rate of inflation will be close to zero during the next five years.  
On December 7, 2013 this model will be presented at the Conference "Inflation Developments after the Great Recession - A Euro Area Business Cycle Network (EABCN)" 
 hosted by the Bundesbank and sponsored by the EABCN.



Catastrophic depopulation in Russia and Japan

The World Bank provides population projections for all countries through 2050. Figures 1 and 2 display the evolution of population in Russia and Japan. Both projections look catastrophic. Population shrinks with acceleration. The birth rate is always lower than the death rate.

Figure 1. Depopulation of Russia and Japan: 2010 to 2050.

Figure 2. The rate of birth and death in Russia and Japan: 2010 to 2050


Russia will never catch up

Labor productivity is the economic parameter that is practically the best to characterize the level of technical development and human capital. The Total Economy Database lists various estimates of productivity for many countries. For Russia,  only GDP per person employed in 1990 Geary-Khamis 1990 $ is available since 1989. For the USSR, the TED provides a virtual time series since 1960. Here, we address the question of evolution of labor productivity in Russian Federation (RF) relative  to the USA. Figure 1 depicts two original curves which reveal the sadness of the current evolution of labor productivity in Russia: the slope of the Russian curve is lower than that for the USA curve. In the long rung these curve diverge. Thus:
Labor productivity in Russia will never catch up that in the USA.
Figure 2 presents a different view on these curves. The difference in labor productivity had been increasing linearly for the USSR and suffered an accelerated increase between 1990 and 2005. A few years of extremely high oil price corrected the deviation down but the current evolution is likely returning to the long term trend - the gap grows with time.
Figure 3 demonstrates that in relative terms labor productivity in Russia is still lower than it was in the USSR. It will take another ten years to get to 1/3 of the US productivity. Discouraging story.
Figure 1. The evolution of GDP per person employed in the USA and Russia.
Figure 2. The differences in labor productivity in the USA and  Russia/USSR.

Figure 3. The ratios of labor productivity in the USA and  Russia/USSR.     



The error of the CPI estimates in Japan runs away

A month ago I wrote about the difference between two inflation measures as expressed by the consumer price index (CPI) and the GDP deflator in Japan. It was shown that the difference between these two indices increases over time. In this post, I add couple graphs to demonstrate that the difference diverges as time squared. Figure 1 displays the difference between the GDP deflator and CPI since 1985. One may observe that the slope of the difference increases with time approximately every five years, i.e. when new basket for CPI is introduced. In Figure 2, we approximate the difference with a quadratic function of time. The difference is running away, i.e. the error in the CPI estimate runs away since the GDP deflator completely includes the CPI.
Figure 1. The difference between the CPI and GDP deflator
Figure 2. Approximation of the difference between the CPI and GDP deflator by a quadratic function of time

Compared with the recent movement of the CPI and that of the GDP deflator, the range of drop of the GDP deflator has been larger than that of the CPI. The discrepancy between the CPI and the GDP deflator is mainly ascribable to the different things they cover. Other causes of the discrepancy include, among others, the different calculation formulae employed.
(1) The Target
While the CPI focuses only on household consumption, the GDP deflator covers business investments in equipment, etc., in addition to household consumption. Since much of the investment in equipment today is made in information technology goods, whose quality is rapidly improving, price falls in such goods considerably affect the deflator. For this reason, the change ratios of the deflator tend to be lower than those of the CPI.
Also, while the prices of petroleum products and other imported goods are rising, the CPI is usually pulled up. On the other hand, the deflator tends to drop until such price hikes are all reflected in the relevant product prices. Thus, the discrepancy between the two grows wider.
If the scopes of the two indices are narrowed down to cover the same items as far as possible, i.e., if we compare the CPI for “All items” with the GDP deflator for “Final household consumption expenditure” alone, the two indices show quite similar fluctuations.

(2) The Formula
While the CPI calculation employs the Laspeyres formula, the GDP deflator employs the Paasche formula. Generally, the Paasche formula, which calculates a weighted average using the quantitative weights at the time of comparison, tends to provide a lower index, while the Laspeyres formula, which employs quantitative weights at reference period, usually produces higher values. In addition, since quality improvement is reflected in the form of an increase in volume, Paasche formula gives a larger weight to an item whose price has fallen due to quality improvement. For this reason, the rate of decline of the GDP deflator, which employs Paasche formula, tends to be getting larger.
Also note that the GDP deflator employs a “chain method” with the reference periods it updates weights annually, to minimize the bias accompanying calculation of the index. Such a chain method is also used with the CPI as well, to provide and publish an additional, referential value to the index.


Does Banque de France control inflation and unemployment?

A PDF version of the paper is available here.

We re-estimate statistical properties and predictive power of a set of Phillips curves, which are expressed as linear and lagged relationships between the rates of inflation, unemployment, and change in labour force. For France, several relationships were estimated eight years ago. The change rate of labour force was used as a driving force of inflation and unemployment within the Phillips curve framework. Following the original problem formulation by Fisher and Phillips, the set of nested models starts with a simplistic version without autoregressive terms and one lagged term of explanatory variable. The lag is determined empirically together with all coefficients. The model is estimated using the Boundary Element Method (BEM) with the least squares method applied to the integral solutions of the differential equations. All models include one structural break might be associated with revisions to definitions and measurement procedures in the 1980s and 1990s as well as with the change in monetary policy in 1994-1995. For the GDP deflator, our original model provided a root mean squared forecast error (RMSFE) of 1.0% per year at a four-year horizon for the period between 1971 and 2004. The same RMSFE is estimated with eight new readings obtained since 2004. The rate of CPI inflation is predicted with RMSFE=1.5% per year. For the naive (no change) forecast, RMSFE at the same time horizon is 2.95% and 3.3% per year, respectively. Our model outperforms the naive one by a factor of 2 to 3. The relationships for inflation were successfully tested for cointegration. We have formally estimated several vector error correction (VEC) models for two measures of inflation. In the VAR representation, these VECMs are similar to the Phillips curves. At a four year horizon, the estimated VECMs provide significant statistical improvements on the results obtained by the BEM: RMSFE=0.8% per year for the GDP deflator and ~1.2% per year for CPI. For a two year horizon, the VECMs improve RMSFEs by a factor of 2, with the smallest RMSFE=0.5% per year for the GDP deflator. This study has validated the reliability and accuracy of the linear and lagged relationships between inflation, unemployment, and the change in labour force between 1970 and 2012.


Володя и мошенники

четыреста ученых написали письмо ВВП, в котором обосновали мошенничество с диссертациями 25 политиков от Единой России.  ВВП недавно заявлял, что политики должны быть кристально честными. Пора действовать! Но пока получается простая и ясная картина: Володя и мошенники - близнецы братья.   


Nasty tricks with inflation in Japan

Consumer price index is a common measure of the overall price change in modern economies, which represents a broader indicator of the cost of living. For Japan, this measure has become extremely important during the last decade because of its very low if not negative value. Economists usually panic when cost of living decreases. Price deflation is a major danger for long term economic growth since bank credits (read investments) are highly suppressed.  Everybody knows: no investment -  no growth.  Hence, any news about positive inflation is welcome and no tricks are bed to get this news.

Figure 1 shows the evolution of two integral inflation measures for Japan: the cumulative CPI inflation and the cumulative GDP deflator (DGDP).  The latter covers all prices in a given economy, while the CPI only a part of them related to consumer goods and services. By definition, the DGDP should nest all CPI items, but the CPI is calculated in a different way based on a basket of goods and products with their shares fixed over a few years. In that sense, the CPI does not follow all economic changes up.

The graphs in Figure 1 are different from inflation indices expressing the absolute change in price level. The cumulative inflation time series describes the relative change in the overall price.  The difference between the DGDP and CPI is dramatic and increases since 1974. From 1974 to 2012, the difference between the cumulative inflation reached 0.37, and the rise in CPI inflation is by ~50%  larger than that in the DGDP.
What is the reason?
The DGDP plays a different role in economics. This is the most important value for real GDP growth. Real GDP is not a measured macroeconomic variable, but rather the ratio of the nominal GDP and the GDP price deflator. Larger DGDP values give smaller real GDP. 

So, this is very important to keep the DGDP as low as possible and the CPI as high as possible. This is a   nasty trick which, mathematically, must result in negative absolute prices of all goods and services not in the CPI.  Do not trust the CPI estimates in Japan. Figure 2 shows two countries without such a dramatic difference between CPI and DGDP: Austria and France. In the USA, the difference between CPI and DGDP has been also increasing since 1978 (Figure3). But the FRB prefers to use Purchase Consumer Expenditures (PCE) instead of the CPI to characterize consumer prices. Figure 4 shows that the DGDP and PCE are similar in the USA. 

Figure 1. The evolution of cumulative rate of CPI inflation and GDP deflator in Japan. 


Figure 2.  The evolution of cumulative rate of CPI inflation and GDP deflator in Austria and France.

Figure 3.  The evolution of cumulative rate of CPI inflation and GDP deflator in the USA.

Figure 4.  The evolution of cumulative rate of PCE inflation and GDP deflator in the USA.