Four months ago we made a prediction of the price of gas at the pump [1]. In essence, we drew a straight line, shown in Figure 1 as the future gas price for the period between March and December 2009. The line represents the difference between core CPI and the motor fuel index. The line says that the price for gas will be growing faster than the price of all goods and services less food and energy (the definition of core CPI).

A midterm review is a natural step in tracking the following prediction [1]:

Unemployment is one of the worst defined economic variables, which ignores the presence of a complete and closed (even if it is considered in many models as an open one) economic system. The rate of unemployment is defined as a share the labor force, i.e. as the percentage of the total available working force actively seeking jobs but remain unhired. As an example, students are not available during scholastic terms and cannot be considered as unemployed regardless of whether they might think about some job position.

The inherent weakness of the definition of unemployment is rooted in the fact that the labor force is a varying portion of the total working age population, as defined by all people of 16 years of age and over. (As mentioned above, this varying portion of the working age population is the denominator in the unemployment rate.) The portion is called labor force participation rate (LFPR). The presence of the varying basis in the definition of the unemployment rate results in a strong bias in the interpretation of the unemployment rate estimates by an unprofessional audience and even many researchers.

Here we are following our previous article on unemployment in Japan, where we predicted the rate to be at 6.0 in August. A new reading 5.4% for June 2009 was published on July 31. At the same time, a new estimate of labor force is also available for June and it is possible to quantitatively predict the unemployment rate using the empirical relationship introduced in the previous article:

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

Three simple graphs illustrating the unemployment situation.

Figure 1 presents monthly growth in the civilian (non-institutional) population (dCP) and in the labor force (dLF) between January 2007 and July 2009. All estimates are obtained in the monthly Current Population Surveys (household data) conducted by the US Census Bureau for the BLS and are seasonally adjusted ones. One can observe three benchmark revisions to the civilian population, which are carried out every January. The estimates of labor force are also affected by the CP revisions. Otherwise, the change in labor force is very volatile. The last three months demonstrate a decrease in the rate of labor force growth from +653 in April to -422 in July. One can expect that the labor force will start to grow again soon.

We have demonstrated that the evolution of various components of CPI and PPI in the United States is not a random but rather a predetermined process with long-term sustainable trends [1-4]. Using these trends, we have predicted consumer and producer price indices for various goods, services and commodities [5-7].

In this post, we present the PPI of three commodities related to metals: steel iron, nonferrous metals, and metal containers. All these items have been already studied in our article [4]. Therefore, it is worth to revisit old predictions and to update them if necessary.

Previously, we have demonstrated that gold ores price can be predicted at a several year horizon [1]. The prediction was based on the observation that the difference between the overall producer price index, PPI, and the producer index for gold ores shows sustainable trends. A new trend has been emerging since 2008 and the difference can be foreseen at a five to ten-year horizon. As the PPI will be likely constant over the next decade, the difference provides a direct prediction of the price index for gold ores. In this article, we update the forecast using a new reading of the PPI for June 2009.

Currently, the US economy is struggling through a period of high volatility in all macroeconomic variables including the difference between various PPI subcategories and commodities. Figure 1 illustrates the observation that this volatile period is related to the turn to a new trend in the difference after 2008. We have made a naïve assumption about the new trend shown by green line - it should repeat the trend observed between 2001 and 2008 but with an opposite sign. Actual trend may be different but inevitably with a positive slope. The green line has a positive slope. Therefore the index for gold ores will be growing at a lower rate than the overall PPI. According to our assumption, the rate of the deviation from the PPI will be +11 units of index per year.

In June 2009, we presented a prediction of crude petroleum price for 2009 (Crude Will Reach $100 by December 2009). Briefly, our analysis has shown that oil will overcome $100 per barrel before the end of 2009. As promised, we evaluate the prediction every month, after new readings of producer price index (PPI) with all its components become available. In this article, we report the results for June 2009.

We continue predicting share prices of S&P 500 companies with Bank of America (BAC). This time, we use an advanced model (see previous model here). The first company studied with this model was Boeing (BA). The advanced model includes four components of the CPI in the USA instead of two at the preliminary stage. For Boeing, the accuracy of prediction has been significantly increased.

One by one, we are modeling share prices of companies from the S&P 500 list. We have already presented quantitative models for companies in various subcategories: information and technology, energy, industrials, consumer staples, materials, and financials (Western Alliance Bancorporation). It is now Bank of America's (BAC) turn.

As before, our pricing model is based on the presence of a linear link between a share price and the difference between various components of CPI. (Share prices are retrieved from YahooFinance and CPI components are from BLS.gov.) The intuition behind the model is simple; a higher pricing power for a given subcategory of goods and services, and thus related companies, is expressed in a faster increase in corresponding stock prices.

We continue modelling share prices of companies from the S&P 500 list. At Seeking Alpha, we have already presented models for the following companies: IBM, DOV, PG, DD, APD, CVX, DVN, HAL, MSFT, COP, and XOM. Here we model Boeing's (BA) share price.

Our pricing model is simple. We assume the presence of a linear link between a share price and the difference between various components of CPI. The intuition behind the model is simple; a higher pricing power for a given subcategory of goods and services, and thus related companies, is expressed in a faster increase in corresponding stock prices. In the first approximation, the deviation between relevant price indices is proportional to the ratio of the pricing powers. The presence of sustainable (linear or nonlinear) trends in the differences, as found in (Kitov, Kitov, 2008; Kitov, Kitov, 2009ab) allows predicting the evolution of the differences, and thus the deviation between prices for corresponding goods and services. The share prices have to follow upwards.

Co-written by Oleg I. Kitov

Abstract

Headline CPI, core CPI, and indices for various small expenditure categories were analyzed. Sustainable long-term linear trends have been found in the difference between the headline CPI and these indices. Overall, the results completely support our previous findings for such principal categories as energy, food, housing, etc. One should return to the relative price of apples and oranges in order to explain these linear trends in terms of the mainstream economics.

Finally, the United Kingdom presents an interesting case, where one can introduce a structural break in the model, i.e. in the predicted time series. Figure 5 displays original and smoothed curves for both observed and predicted GDP between 1972 and 2009. There is a distinct break around 1991 in both time series, which is described by a step in A from $400 to $500. The curve before 1991 is also shifted by 1 year relative to that after 1991. This implies the change in the specific age, which is nine years after 1991.

Both segments of the predicted curve explain the 1991 recession. Because of the break, the 1991 reading can not be modelled. Since 1995, the observed growth rate has been hovering around 2% per year. It is a very difficult time series to model. The absence of changes with time means that any variable constant over the same period perfectly explains the observed pattern. Even the smallest difference of 0.5% is seen as a larger deviation, as the right panel of Figure 5 shows. In any case, the rate of GDP growth in the UK will likely remain above the zero line.

Following France and Germany is New Zealand.

The model for New Zealand is also obtained by trial-and-error method. Empirical constant A and the specific age have been varied in order to fit amplitude and major features of the observed curve. The best fit value is A=$220 (1990 US$), i.e. less than in France and Germany. The specific age population in New Zealand is 14 years, which is different from that in the US, Japan, France, and Germany. The age pyramid enumerated by the 2006 census was extrapolated in the past and in the future in order to estimate the number of 14-year-olds in (4).

This is a continuation of the previous post, where the model is presented.

In alphabetical order, the next country to predict the evolution of real GDP per capita is Germany. The best fit constant A=$260 and the defining age is eighteen years. The age distribution from 2002 allows a prediction at an 18-year horizon. From Figure 3, one can expect a slow-down in 2009 and likely a recession in 2011. Here, we would like to accentuate that the prediction of the 2009 slowdown could be obtained in 2002, i.e. seven years before it happened! The estimates of population age structure are slightly noisy, however. Otherwise, the agreement between the observed and predicted curves is excellent after some years of turbulence associated with the reunification.

In 2005, we introduced a new concept describing the evolution of real Gross Domestic Product (GDP) as driven by the change in specific age population and the attained level of real GDP per capita [3-6]. According to this model, the growth in real GDP per capita (for the sake of brevity, below we often omit “per capita”) in developed countries is characterized by an annual increment, as expressed in dollars per year, which is constant over time, and all fluctuations around the long-term trend defined by the increment can be explained by the change in the number of people of country-specific age population. Therefore, real GDP would be growing as a linear function of time, when no change in the population of relevant age is observed. As a rule, in Western Europe the cumulative growth in the specific age population during the last 60 years is negligible and thus the cumulative input of the population component is close to zero. In the USA, the overall increase in the specific age population is responsible for about 20% of the total growth in real GDP since 1960 [4]. The presence of constant increment implies that the rate of growth of real GDP is an inverse function of the attained level of real GDP itself.

Our model of real economic growth was first derived from data for the United States [3] and Japan [7]. Since all GDP time series are intrinsically non-stationary ones we have conducted a comprehensive statistical analysis including tests for cointegration [6]. Both the Engle-Granger and Johansen approaches confirmed the presence of a cointegrating relation between real GDP and the specific age population, which is nine years in the USA and eighteen years in Japan. In this paper, we demonstrate the possibility to predict the evolution of real GDP in France, Germany, New Zealand, and the United Kingdom. Due to shorter time series for these countries, no econometric (statistical) techniques are used to validate the concept except obvious visual fit between dynamic and cumulative time series.

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

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.

The model

As originally defined by Kitov (2006a), inflation and unemployment are linear and potentially lagged functions of the change rate of labor force:

Abstract

Potential links between inflation, p(t), and unemployment, UE(t), in Germany have been examined. There exists a consistent (conventional) Phillips curve despite some changes in monetary policy. This Phillips curve is characterized by a negative relation between inflation and unemployment with the latter leading the former by one year: UE(t-1) = -1.50p(t) + 0.116. Effectively, growing unemployment has resulted in decreasing inflation since 1971, i.e. for the period where GDP deflator observations are available. The relation between inflation and unemployment is statistically reliable with R²=0.86, where unemployment spans the range from 0.01 to 0.12 and inflation, as represented by GDP deflator, varies from -0.01 to 0.07.

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.