Five years ago I published a paper on the link between inflation and labor force in the U.S. There was one stupid problem with the estimates of labor force level provided by the BLS. They competely ignored the changes in so called population controls after decennial censuses. Briefly, any census reveals the difference between projected and directly enumerated populations. The former figures are projected from the previous census using estimated birth and death rates and net immigration. After any census, this difference called "the error of closure" is proportionally distributed by the U.S. Census Bureau (CB) over the previous decade. This prodecure makes all population time series reported by the CB smooth.
The BLS does not address this problem at all. Therefore, its labor force time series has several "bumps" of a million an more people per one month. One should not use this time series as it is in statistical of econometric assessments. I had to redistribute all known bumps back into their past and obtained relatively smooth time series. This simple procedure did not work well in 1991 and additional investigation was needed to recover the reason of ~1,000,000 step in the labor force series.
Recently, I have found a paper written by Marisa Di Natale "Creating Comparability in CPS Employment Series" from the BLS (no publication date is specified). The author used the same method of the error of closure redistribution. It is a good paper with a simple but correct methodology. But do not believe it. The original time series has not been smoothed. I downloaded the most recent version of labor force time series )July 1, 2011) and found no changes in 1991 and 2001. Same sharp spikes:
Figure. Monthly increment of labor force in the U.S.
Conclusion: Do not trust BLS!
7/8/11
7/7/11
On the future rate of unemployment in the US
The current status and near future of inflation and unemployment in the U.S. has ignited fierce debates on the reasons behind both macroeconomic variables. There are quite a few academic economic models with the Phillips curve in the centre of many. Any academic knows that unemployment is likely among the principal driving force behind inflation. To be able for this mission the change in the rate of unemployment must be contemporaneous or lead the induced changes in the rate of inflation. This is a dogma despite observations contradict all such models.
Six years ago, we presented a model which links labour force to unemployment and inflation. In other words both variables are driven by the only force – the change in labour force. Year by year, this model has been providing accurate predictions in all biggest developed countries. Econometrically, the change in labour force is cointegrated with the rate of inflation and unemployment, as the Engle-Granger and Johansen tests have shown. Since both variables have the same root, we introduced a generalized model. This model links all three variables together. It also allows resolving some metrological and methodological problems and improving prediction. Unfortunately for the mainstream models, measurements show that unemployment lags behind inflation and labour force by 2.5 and 5 years respectively. In that sense, we called our model the anti-Phillips curve, i.e. the curve with reversed causality. The original model covered the whole period between 1963 and 2005 by one empirical relationship. It provided a much better prediction of inflation and unemployment than any other model (by a factor of 2 lower RMFSE at a two year horizon). Among other predictions, we foresaw the possibility of a deflationary period from 2012 with very low inflation after 2010. The original model is now enhanced by the introduction of structural breaks related to the major changes in monetary policy. Such structural breaks are manifested in the change of coefficients in the linear links between inflation, unemployment and labour force. The linearity is retained, however. For the U.S. we have obtained the following model:
ut = 1.2lt-5 + 0.21pt-2 +2.03; 1984>t≥1956
ut = 1.9lt-5 + 0.425pt-2 +1.53; 2008>t≥1984
ut = 3.4lt-5 + 0.84pt-2 + 2.00; t≥2008
where ut is the rate of unemployment at time t; lt-5 the relative change rate of labour force (= dlnLF/dt) five years before , t-5; pt-2 is the rate of (CPI) inflation two years before. There are two breaks in 1984 and 2008. Both are obtained by a free search minimizing the RMS model error between 1960 and 2010. One can guess that the change in monetary policy increases the sensitivity of unemployment to the change in labour force and inflation approximately by a factor of 2.
Figures 1 through 3 present the measured and predicted rate of unemployment and the model error. Considering the fact that the rate of unemployment is predicted at a two and a half year horizon the agreement is excellent. Of special importance is the current peak in unemployment which follows from the change in monetary policy in 2008.
Figure 1. Monthly observed and predicted rate of unemployment in the U.S. One can expect the rate to fall below 8% by the end of 2011 or in early 2012.
Figure 2. Annual observed and predicted rate of unemployment in the U.S. One can expect the rate to fall below 8% by the end of 2011 or in early 2012.
Figure 3. The annual model error. The model predicts at a two year horizon. We also present cumulative annual error between 1965 and 2010.
Six years ago, we presented a model which links labour force to unemployment and inflation. In other words both variables are driven by the only force – the change in labour force. Year by year, this model has been providing accurate predictions in all biggest developed countries. Econometrically, the change in labour force is cointegrated with the rate of inflation and unemployment, as the Engle-Granger and Johansen tests have shown. Since both variables have the same root, we introduced a generalized model. This model links all three variables together. It also allows resolving some metrological and methodological problems and improving prediction. Unfortunately for the mainstream models, measurements show that unemployment lags behind inflation and labour force by 2.5 and 5 years respectively. In that sense, we called our model the anti-Phillips curve, i.e. the curve with reversed causality. The original model covered the whole period between 1963 and 2005 by one empirical relationship. It provided a much better prediction of inflation and unemployment than any other model (by a factor of 2 lower RMFSE at a two year horizon). Among other predictions, we foresaw the possibility of a deflationary period from 2012 with very low inflation after 2010. The original model is now enhanced by the introduction of structural breaks related to the major changes in monetary policy. Such structural breaks are manifested in the change of coefficients in the linear links between inflation, unemployment and labour force. The linearity is retained, however. For the U.S. we have obtained the following model:
ut = 1.2lt-5 + 0.21pt-2 +2.03; 1984>t≥1956
ut = 1.9lt-5 + 0.425pt-2 +1.53; 2008>t≥1984
ut = 3.4lt-5 + 0.84pt-2 + 2.00; t≥2008
where ut is the rate of unemployment at time t; lt-5 the relative change rate of labour force (= dlnLF/dt) five years before , t-5; pt-2 is the rate of (CPI) inflation two years before. There are two breaks in 1984 and 2008. Both are obtained by a free search minimizing the RMS model error between 1960 and 2010. One can guess that the change in monetary policy increases the sensitivity of unemployment to the change in labour force and inflation approximately by a factor of 2.
Figures 1 through 3 present the measured and predicted rate of unemployment and the model error. Considering the fact that the rate of unemployment is predicted at a two and a half year horizon the agreement is excellent. Of special importance is the current peak in unemployment which follows from the change in monetary policy in 2008.
Figure 1. Monthly observed and predicted rate of unemployment in the U.S. One can expect the rate to fall below 8% by the end of 2011 or in early 2012.
Figure 2. Annual observed and predicted rate of unemployment in the U.S. One can expect the rate to fall below 8% by the end of 2011 or in early 2012.
Figure 3. The annual model error. The model predicts at a two year horizon. We also present cumulative annual error between 1965 and 2010.
How useful are working papers?
I am a fan of working papers. Berk Oezler has a post on the usefulness of working papers.
In my view, there are several advantages which can not be provided by reviewed paper journals.
1. Science is an iterative process and no result is perfect and final. This is the responsibility of the author
to present results as accurate as possible. Authors providing poor results become marginal quickly.
2. Double-blind review is a good thing to keep real findings far away from interested audience. I can refer to the complaints of the Nobel Prize laureat Prescott about the sad story of his publication with another Nobel Prize laureat Kyndland. The paper they were given the prize for was rejected by reviwers in many journals. It was first published as a working paper. Katarina Juselius also complains about enormous rate of rejections in American journals for european economists. Clear bias.
3. As a scientist, I am interested in new findings and ideas as soon as possible. I can judge myself what I need and what I do not need without reviewers, who might not be qualified to assess fine details. Working papers are usually published in electronic format which allows a prompt access and effective machine search.
4. Last but not the least. Articles in major journals are not free. I understand the reason. However, working papers contain same information for free.
In my view, there are several advantages which can not be provided by reviewed paper journals.
1. Science is an iterative process and no result is perfect and final. This is the responsibility of the author
to present results as accurate as possible. Authors providing poor results become marginal quickly.
2. Double-blind review is a good thing to keep real findings far away from interested audience. I can refer to the complaints of the Nobel Prize laureat Prescott about the sad story of his publication with another Nobel Prize laureat Kyndland. The paper they were given the prize for was rejected by reviwers in many journals. It was first published as a working paper. Katarina Juselius also complains about enormous rate of rejections in American journals for european economists. Clear bias.
3. As a scientist, I am interested in new findings and ideas as soon as possible. I can judge myself what I need and what I do not need without reviewers, who might not be qualified to assess fine details. Working papers are usually published in electronic format which allows a prompt access and effective machine search.
4. Last but not the least. Articles in major journals are not free. I understand the reason. However, working papers contain same information for free.
7/2/11
Pepco Holdings on rise - revisited
In April 2011, we introduced a new model for Pepco Holdings (POM) and predicted its share to rise. The defining CPI indices were as follows: the index of food away from home (SEFV) and the index of owners' equivalent rent of residence (ORPR). The CPI components are leading by 4 and 5 months, respectively. The best fit model, i.e. the lowermost RMS residual error, between July 2010 and March 2011:
Overall, our prediction from April 2011 was correct and we are going to revise the POM model for Q3 2011 when all relevant CPI readings are available.
F igure 1. Observed and predicted POM share prices.
POM(t) = -2.66SEVF(t-4) +1.06ORPR(t-5) +11.83(t-1990) + 101.35
where POM(t) is the share price in U.S. dollars, t is calendar time.
We predicted that “In the second quarter of 2011, the model foresees a rise by $1.5.” Actual monthly closing price has increased from $18.55 in March to $19.63 in June 2011. The predicted price is well within the high/low monthly bounds, i.e. practically within the uncertainty bounds of the POM price.
AFLAC share price in Q2 2011
It’s time to revisit our stock price model for Aflac Incorporated (AFL). This is a financial company in the S&P 500 list. We predicted the price using the CPI estimates published by the BLS on April 14, 2011. It was a preliminary model. The share price was defined by the consumer price index of household furnishing and operations (HFO) and that transportation services (TS). The defining time lags are as follows: the HFO index leads the share price by 2 months and the TS by 4 months. The best-fit 2-C model for AFL(t) was as follows:
AFL(t) = -5.02HFO(t-2) – 2.87TS(t-6) + 20.42(t-1990) + 997.71
where AFL(t) is the AFL share price in U.S. dollars, t is calendar time.
We will model the evolution of the AFL price in Q3 2011 when the CPI components of the model are published by BLS.
Figure 1. Observed and predicted AFL share prices.
7/1/11
SunTrust Banks share in Q2 2011
In April 2011, we made the following prediction for SunTrust Banks (STI) share price: “… In the second quarter of 2011, the price may drop to the level of $23 (and then to $18) from the current $28“.
This prediction was obtained from a model based on the consumer price index of food less beverages (FB) and the index of tobacco and tobacco products (TOB). The former defining CPI component led the share price by 4 months and the latter one by 6 months. Therefore, the model has a natural 4-month forecast horizon.. The best-fit 2-C model for STI(t) is as follows:
STI(t) = -5.46FB(t-4) – 0.19TOB(t-6) + 36.07(t-1990) + 627.06
where STI(t) is a share price in US dollars, t is calendar time. Figure 1 displays the model and actual prices. The STI actual price has fallen from $28.83 in March to $25.8 in June 2011. We have overestimated the fall but it is still within the uncertainty bounds defined by high/low monthly prices. We expect the price to fall in July 2011.
Figure 1. Observed and predicted STI share prices with monthly high/low (adjusted) prices
On the successful prediction of the HPQ share price in Q2 2011
Three months ago, we presented a model for HPQ stock price based on the decomposition into a weighted sum of two CPI components. We predicted the evolution of the monthly closing price (adjusted for dividends and splits) four months ahead using the CPI estimates published by the BLS on April 14.
The long term model is defined by the index of food without beverages (FB) and that of rent of primary residency (RPR). The former CPI component leads the share price by 4 months and the latter one leads by 5 months. Figure 1 depicts the overall evolution of both involved indices through March 2011. The best-fit 2-C model for HPQ(t) was as follows:
HPQ(t) = -3.34F(t-4) + 3.41RPR(t-5) + 0.51(t-1990) – 85.44
The predicted curve is shown in Figure 2 and covers the period between July 2003 and March 2011. In the second quarter of 2011, the model predicted the share price to fall to the level of $37 in June 2011 and then to $33 by the end of July 2011. Figure 3 demonstrates that this prediction was almost correct and the closing price of June 2011 is $36.4.
The U.S. Bureau of Labor Statistics will publish the estimates for the involved CPI components for June 2011 only in the middle of July. We are going to revise the current HPQ model and publish our prediction for the third quarter of 2011. Meanwhile, we expect a HPQ share to fall in July 2011 to the level of $33.
Figure 1. Evolution of the price of FB and RPR.
Figure 2. Observed and predicted HPQ share prices in March 2011. The contemporaneous prediction is shown by red line. Black diamonds present the original line shifted 4 months ahead, i.e. the model. We expect the price to fall down to $33 in July 2011.
Figure 3. The evolution of a HPQ share price as predicted in March 2011 and the actually observed monthly closing price (adjusted for dividends and splits) between April and June 2011.
Update.
The monthly closing price is one of many measures of stock prices. One can average daily or hourly prices over one month and model them instead of the monthly closing price. Therefore, the predicted prices should be considered in the framework of the uncertainty in actual prices. Figure 4 depicts the predicted and observed monthly closing prices for HPQ together with (adjusted) monthly low and high prices. The predicted price is well inside the uncertainty of the observed one but four months ahead of it.
Figure 4. Monthly observed and predicted closing prices together with monthly low and high prices.
Update.
The monthly closing price is one of many measures of stock prices. One can average daily or hourly prices over one month and model them instead of the monthly closing price. Therefore, the predicted prices should be considered in the framework of the uncertainty in actual prices. Figure 4 depicts the predicted and observed monthly closing prices for HPQ together with (adjusted) monthly low and high prices. The predicted price is well inside the uncertainty of the observed one but four months ahead of it.
Figure 4. Monthly observed and predicted closing prices together with monthly low and high prices.
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