Copper and aluminum

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 (the previous revision was two years ago). 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 somewhere 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). Two years ago we predicted that the PPI of copper might go any direction into 2014, but did not exclude further fall in the PPI of copper relative to the overall PPI. Currently, there is no sign that the PPI of copper is going to change its long-term decline.

On the other hand, aluminium has changed the price evolution dramatically, as Figure 3 shows. We expected the difference to follow the green line into 2016, but this commodity suddenly changed its behavior and the price of aluminum started to grow in 2014. Currently, the price of aluminum follows a linear trend, which is almost a mirror reflection of the expected growth. Same may happen to copper, which is not a well predictable commodity in the long run, but evolve along short linear segments. 

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

Figure 2. Evolution of the difference between the overall PPI and the price index of copper ores normalized  to the PPI.

Figure 3. Evolution of the difference between the overall PPI and the price index of aluminum scrap normalized to the PPI.  

$22 per barrel and world's future

In 2009, we  forecasted the price of crude (WTI) in 2015 and 2016 several times since  [1, 2, 3]. These estimates varied between $22 and $60 per barrel in 2016. A few days ago, Saudis  put forward a strong message that $20 per barrel will be bearable for them in order to retain their share in the global oil market. This message is in line with our  prediction from June 26, 2009 of $22  per barrel in 2016. This also means that the oil price war is actually ahead - it's a long way down to 1/3 of the current price. It is clear that the world's economy and global political structure will be dramatically reshaped in the second part of the 2010s. 

It is time to formulate hard questions.

How the US government will tackle consumer price deflation when energy (more than 10% of the headline CPI) falls by a factor of 2?

What will happen to the 2016 US  elections with the oil (and other resources) lobby without money?

How the role of China will change with cheap energy? 

The French economy needs much more (and even more) money to boost labour force growth and inflation

A year ago we published a paper “Does Banque de France control inflation and unemployment?” We demonstrated that the French economy would likely sink into a longer period of deflation or very low inflation rate after 2013. This is an excerpt from the paper discussing how Banque de France could boost labour force growth and inflation by flooding the French economy with money. Instead of this simple measure there were several depressing years of contingency measures introduced by the ECB. Now the money issue is likely on the table and we repeat our analysis.

Here, we consider the rate of inflation, unemployment, and the change in labour force altogether. For France, the generalized relationship is obtained as a sum of (10) and (13), which results, with some marginal tuning of all coefficients in order to reduce the standard error of the model, in the following equation for the GDP deflator:

π(t) = 2.69l(t-5) - u(t-5) + 0.108;      1971≤t≤1995                                                              

π(t) = 6.40l(t-5) - u(t-5) + 0.059;                t≥1996                                                 (14)

For the OECD CPI:

π(t) = 3.0l(t-5) - u(t-5) + 0.108;      1971≤t≤1995                                                               

π(t) = 5.0l(t-5) - u(t-5) + 0.067;                 t≥1996                                                  (15)

where we model inflation since it lags by 5 years behind the change in labour force and unemployment. Formally, one can re-write both relationships for u(t). Notice that the change in the slopes and intercepts are much smaller than in individual relationships. The structural break is less prominent and thus its estimate is less reliable.  

The annual and cumulative curves for both cases are presented in Figure 12.  Linear regression of the observed inflation against that predicted according to (14) and (15) is characterized by outstanding for annual curves statistical properties: R²=0.87 and RMSFE=0.015 y^-1, and R²=0.83 and RMSFE=0.017 y^-1, respectively. For the cumulative curves, both R² are larger than 0.99 and RMSFE~0.025 y^-1, i.e. by 20% smaller than the naive ones (see Table 4). These estimates were obtained for the period between 1972 and 2012 with a five-year lag. These RMSFEs are the best obtained for France at a five year horizon so far. They explain the rate of price inflation to the extent beyond which measurement uncertainty should play the key role. Practically, there is no room for any further improvements in R² given the accuracy of the current prediction.

Conclusion

We have successfully modelled unemployment and inflation in France. Their sensitivity to the change in labour force requires very accurate measurements for any quantitative modelling to be reliable. Unfortunately, the OECD labour force time series does not meet this requirement and poor statistical results are obtained for annual readings. The best prediction is obtained with the moving average technique applied to the change in labour force. For the period between 1970 and 2012, linear regression analysis provides R² as high as 0.8 to 0.9 for the rate of unemployment and GDP deflator. The RMSFE for the best CPI model is 0.015 y^-1 and 0.010 y^-1 for the GDP deflator, both at a four year horizon. For the period after 1994, the best RMSFE=0.005 y^-1 for both measures of inflation. In 1994, our models have structural breaks found by the OLS fit. For the VECM representation, the standard error for the GDP deflator is as low as 0.010 y^-1 at a four year horizon and 0.005 y^-1 for a two year horizon. The whole period and 0.004 y^-1 for the period after 1994. All in all, we have obtained a very accurate description of unemployment and inflation in France during the past 40 years.   

Having discussed the technically solvable problems associated with the uncertainty in the labour force measurements, we start tackling the problem associated with the divergence of the observed and predicted curves starting around 1995.  An understanding of this discrepancy is a challenge for our concept. Potentially, these curves diverge due to the new monetary policy introduced by the Banque de France. We may claim that the policy of constrained money supply, if applied, could artificially disturb relationships (9), (10), and (13). We had to introduce a structural break and to estimate new coefficients after 1995 for unemployment and after 1994 for inflation, respectively. These coefficients are less reliable because the relevant time series are short and vary in narrow dynamic ranges, but they are definitely different from those before the breaks. One could conclude that Banque de France has created some new links between the unemployment, inflation, and labour force, shifting coefficients in the original long term equilibrium relations. 

Figure 12. Comparison of the observed and predicted inflation in France (DGDP and CPI) - annual and cumulative inflation since 1972. The predicted inflation is a linear function of the labour force change and unemployment.

We think that the true money supply in excess of that related to real GDP growth should be completely controlled by the demand related to the growing labour force. This excessive money supply is accommodated in developed economies through employment growth, which then causes price inflation. The latter serves as a mechanism effectively returning the normalized personal income distribution to its original shape (Kitov and Kitov, 2013). The relative amount of money that the economy needs to accommodate through increasing employment, as a reaction on independently growing labour force, is constant through time but varies among developed countries. This amount has to be supplied to the economy by central bank.

The ESCB limits money supply to achieve price stability. For France, the growth in labour force was so intensive after 1995 that it requires a much larger money supply for creation of an appropriate number of new jobs. The 2% artificial constraint on inflation, and thus on the money supply, disturbs relationships (10) and (13). Due to lack of money in the French economy, the actual (and mainly exogenous) growth in labour force was only partially accommodated by 2% inflation. The lack of inflation resulted in increasing employment. In other words, instead of 2% unemployment, as one should expect according to the relationship before 1995, France had 9% unemployment. Those people who entered the labour force in France in excess of that allowed by the target inflation rate had no choice except to join unemployment in order to compensate the natural 7% rate of inflation, which was suppressed to 2%.

The lags and amplification factors (sensitivities) found for unemployment and inflation in France are quite different from those obtained for the USA and Austria (Kitov and Kitov, 2010). The latter country is characterized by the absence of time lags and low sensitivities. In the USA, inflation lags by two and unemployment by five years behind the change in labour force, with sensitivities much lower than those in France. Apparently, the variety of lags is the source of problems for the Phillips curve concept.

The causal link between inflation, unemployment, and labour force gives a unique opportunity to foresee future at extra long time horizons. The accuracy of such long-term unemployment and inflation forecasts is proportional to the accuracy of labour force projections. For example, central banks can use labour force projections as a proxy to “inflation expectation” in their NKPCs. Figures 8 and 12 imply that France will be enjoying a period of low inflation rate in the near future. Monetary policy of the ECB is also an important factor for these forecasts because of its influence on the partition of the labour force growth between inflation and unemployment. Moreover, this is the responsibility of the ECB and Banque de France to decide on the partition.

The era of low energy price has come

Two years ago we published a post “The era of low energy prices”.  This era has come.  Here we just repeat the post with updated figures, which include now the data since 2012.

The long-term evolution of energy prices affects the fundamental environment for stock prices. When consumer prices for various goods and services have different but sustainable trends relative to energy prices an opportunity arises for sound investments. Our observations show that some of these sustainable trends have clear turning points which provide investors with invaluable information on buy/sell decision. In this article, we investigate the past and future evolution of the consumer price index (CPI) of energy and demonstrate that it may fall sharply in the near future.  

Six 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., the CPI less food and energy) and the overall CPI. Then the consumer price index of energy, which gives approximately 9% of the headline CPI, was analyzed. In the beginning of 2008, we tentatively identified a turning point in the difference between the CPI and the energy index and predicted energy prices to fall relative to the core CPI through the first half of the 2010s. Here we revisit this prediction and demonstrate the turning point timing and the duration was relatively accurate.

Here we study the relative evolution of the core consumer price index (CPI) and the CPI of energy. Figure 1 displays the difference between the core CPI and the index for energy for the period between 1960 and November 2014. All CPIs are seasonally adjusted and borrowed from the BLS.  Before 1980, these two indices had been growing almost in sync with fluctuation around 10 units of price index. Between 1981 and 1999, the difference grew from -10 to almost 80 units. Between 2001 and 2008, a period of intensive growth in the energy index was observed. Qualitatively, one can distinguish three periods of linear trend and three turning periods with a higher volatility. The last turning point was in 2008 and the index of energy is likely on a declining path relative to the core CPI.  However, the extremely high volatility masks the new trend in the difference.

The past few months have revealed a new statistically significant trend – the difference started to grow at a higher rate. This is a clear manifestation of the new, low-energy-cost, era.

Figure 1. The difference between the core CPI and the index for energy between 1960 and 2014. There are three periods of linear trend and three turning periods. The most recent turning point was in 2008.

Figure 2 provides a detailed view of the most recent period. The energy index grew much faster than the core CPI between 2001 and 2008. Linear regression gives a slope of -14 for the difference curve. This assumes that the energy index grew by 14 units faster every year than the core CPI.  Since August 2005, the energy price volatility has been at an elevated level and one can likely classify the past seven years as a period of bifurcation. In 2012, there was no clear indication of the direction and slope of the next linear trend. At the same time, we did not expect any further increase in oil price beyond that dictated by the overall price increase. We also expected the current volatility period is close to its natural end and the difference of the core and energy CPI will be growing along the new trend shown by green line in Figure 2. 

Now we see the price of oil falling and driving the price of energy in the CPI down. The difference of the CPI and energy index has been rising since July-August and the difference is currently above the green (trend) line. The question is how long will last the period of cheaper energy and how low will be the price fall.

Figure 2. Same as in Figure 1, for the period after 2002. Linear trends are shown. 

  

We used only absolute difference so far. It is instructive to analyze the difference in relative terms and we have normalized the difference to the core CPI. Figure 3 illustrates the new pattern. In contrast to Figure 1, the amplitudes and periods of long term fluctuations are similar and the overall evolution seems to be repeatable. Figure 4 exercises the assumption of repeatability. We have shifted the original curve by 27 years ahead and obtained a striking similarity in the amplitude and timing of the energy price falls and rises. Figure 5 shows a detailed picture. From the red curve, we expected in 2012 an energy cliff, as it had been observed 27 years ago. We expected the era of low energy prices to come.    

If the history repeats itself we will observe very low energy prices, the price index of energy will drop by 50 points from the November 2014 level or by ~20%, in a year or less. This era will last several years, at least.

Figure 3. The difference between the core and energy CPIs normalized to the core CPI.

Figure 4. Same as in Figure 3 with red curve representing the original (black) curve shifted 27 years ahead.

Figure 5. The energy cliff has come and energy price experience free fall?