Trump supporters have to thank Big Tech ban

 The Big Tech ban was perceived nervously by Trump supporters as likely not complying with freedom of speech's highest standards. In my view, they have to thank Big Tech - in the absence of messages from Trump, there was no room for interpretation of his words. During mostly peaceful BLM demonstrations, there were some provocateurs who were presented as the participants of the peaceful process. There are also people who are waiting for any word or action from Trump to act in any direction they want. There is no way to screen out such people in big crowds.

Big Tech actually save Trump from the worst scenario. It seems their action was a sincere wish to assist Trump.

To Trump or not to Trump

I was involved in the analysis of mechanical and seismic effects of underground nuclear tests - from small to 150 kt. It helps me now to be more effective in monitoring the non-testing regime. Thirty five years ago, we developed a mechanical model with a sleeping hierarchical structure of inhomogeneities in various rock types. An underground nuclear explosion creates an extremely high-amplitude shock wave damaging the surrounding rocks. The sleeping hierarchical structure, i.e. a system of healed cracks and discontinuities,  is excited by the shock wave, emits (induced) elastic waves, and we measure these waves by seismometers. The features of these seismic waves allow recovering the features of the sleeping hierarchical structure.

Trump is an extremely powerful source of energy damaging the internal structure of the US political and governmental systems. He is definitely not a part of this system and his actions were so high-amplitude that the system was excited and started to send signals to the outer world. We even do not need to measure them with precise equipment (but we have to) – they are visible and striking. It is not for me to judge is it for better or worse for the USA.

I have just academic interest in the active impact on physical and social systems. The COVOD-19 pandemic, for example, is high stress for the economic system and the response of the US economy has to be studied in tiny details in order to understand the real structure and processes underlying economic activity. This also helps to distinguish between quantitatively accurate and quantitatively worthless economic models. In this blog, I have presented some findings associated with the pandemic.

The experience with active impact on various systems and the study of their reaction gives me some confidence that we have to Trump (in the sense we have to beat and hit). When we do not Trump, we are missing important information on the vital system features. In social sciences, Gorbachev and Yeltsin were two Trumps in the Soviet Union and Russia. We have learned a lot about the governing system and people. Not all findings were pleasant. But now we have no illusions about friendship and cooperation - no such things between businesses and countries.  

Conservatives should not bother about liberals. They pretend to be Alphas from "Brave New World", and thus, destroy each other

Brave New World

Mustapha Mond smiled. "Well, you can call it an experiment in rebottling if you like. It began in A.F. 473. The Controllers had the island of Cyprus cleared of all its existing inhabitants and re-colonized with a specially prepared batch of twenty-two thousand Alphas. All agricultural and industrial equipment was handed over to them and they were left to manage their own affairs. The result exactly fulfilled all the theoretical predictions. The land wasn't properly worked; there were strikes in all the factories; the laws were set at naught, orders disobeyed; all the people detailed for a spell of low-grade work were perpetually intriguing for high-grade jobs, and all the people with high-grade jobs were counter-intriguing at all costs to stay where they were. Within six years they were having a first-class civil war. When nineteen out of the twenty-two thousand had been killed, the survivors unanimously petitioned the World Controllers to resume the government of the island. Which they did. And that was the end of the only society of Alphas that the world has ever seen."

Statistics is in danger as a discipline after the US election irregularities

In 2012, I wrote several posts on the irregularities in voting distributions after the Duma elections.  There were many scientists with a strong background in statistics applied in physics, and I am not the best among them.  The outcome of numerous studies revealing various types of potential falsifications as expressed in voting irregularities is well known  - the Russian elections are more open and better organized. For example, Navalny lost the 2013  Moscow mayoral elections with 27% of votes. 

The 2020 US elections were criticized by many statisticians from the same position of statistical irregularities in the voting results and timing of major changes. I have no access to voting data sets to analyze them in the same way as I did in 2012.  Hope they will be open and published for deeper analysis by the statistical community. On the other hand, the results of such studies are highly likely to be banned by the Big Tech and the US universities have a propensity to fire professors and researchers not following their ideology.

I have a strong feeling that statistics as a discipline is in danger in the US. If it does not study the voting irregularities is loses its own credibility. If it will study the irregularities - the statisticians may suffer undeserved punishment. Zugzwang.

The link between unemployment and real economic growth in the UK. Brits are able to retain data quality and integrity

 In 2011, we published a paper modeling the link between the annual change in employment rate and the change rate in the real GDP per capita. This paper also included modeling of a similar link for unemployment.  In 2011, we used data from various sources – the OECD, BEA, BLS, and the Total Economy Database of the Conference Board. Because of the data availability, the period under consideration varied between countries and the longest time series started between 1950 and 1960. For many developed economies, however, the start year was 1970. Since the studied period was limited to 2010, we promised to revisit all published models and validate the original versions. In the beginning of 2021, we have new estimates of the involved parameters between 2010 and 2019 and re-estimate the unemployment models. In this post, we revise the model for the United Kingdom.  

 

As in hard sciences, the standard modeling procedure is to check data quality. As we learned before, the GDP deflator (dGDP), which is a principal component of the real GDP estimation procedure, is prone to artificial breaks induced by definitional revisions. Such breaks in the GDP time series look like the structural breaks related to inherent changes in economic performance.  When taken as real, the definitional breaks ruin the statistical performance of the mainstream economic models.

 

The UK has likely the most developed school in hard and soft sciences. Data quality is the essence of experimental sciences in support to theoretical consideration. Economics is not an exception and the quality and compatibility of measurements is generally retained. In panel a) of Figure 1, we present the evolution of the cumulative inflation (the sum of annual inflation rates) as defined by the CPI and dGDP between 1955 and 2018. Both variables are normalized to their respective values in 1955, and thus the curves start from 1.0. One can see that the curves are close, but deviate from the very beginning.  In panel b), the rates of price inflation are shown as calculated using the CPI and dGDP indices. The only large deviation between the inflation curves was in 1994 and is likely artificial (corresponding documentation may describe the reasons behind the spike, but its nature is not important for this post). This spike is pretending to be described with a dummy variable, which we did not use in the previous models.

 

Panel c) in Figure 1, depicts the difference between the cumulative and change rate curves in panels a) and b). The cumulative curves demonstrate approximately linear deviation since the late 1970s. Taking into account the quasi-linear deviation between the cumulative curves, we propose the following piece-wise model for the best fit between the CPI and dGDP curves:

 

CPI = 0.926dGDP,        1970>t≥1960

CPI = 0.974dGDP,        2018≥t≥1971, except 1994  

CPI = 0.974dGDP - 0.049,     t=1994                     (1)

 

Panel d) illustrates the match between the cumulative curves, which include a dummy variable of -0.049 in 1994. The spike is removed. The model residual is presented in panel e), where the standard deviation is 0.018 for the period between 1957 and 2018. The CPI and dGDP comparison indicates that there are potential breaks in the dGDP curve in the early1970s, the late 1980s, and likely between 2009 and 2012.

 

a)        

b)

c)

d)

e)

 

Figure 1. a) The evolution of the cumulative inflation (the sum of annual inflation rates) as defined by the CPI and dGDP between 1956 and 2018. Both variables are normalized to their respective values in 1956.  b) Inflation rates for the CPI and dGDP. c) The difference between the CPI and dGDP curves in panels a) and b). d) The fit between the CPI and the dGDP corrected according to relationship (1). e) The residual error of model (1).

 

The break years obtained from the dGDP are used as start points for the search of the best fit in our version of Okun’s law for the UK. We are using the standard LSQR procedure to estimate a new set of coefficients for the period between 1963 and 2018. The data before 1963 are not used because the rate of inflation is very close to 1%, and is difficult to model. The preliminary analysis gives the following model:           

du_p = -0.63dlnG + 1.75,  1987>t≥1963

du_p = -0.42dlnG + 0.64,  2010≥t≥1988          

du_p = -0.39dlnG  - 0.13,           t≥2011                     (2) 

Figure 2 illustrates the model predictive power. In the upper panel, the measured rate of unemployment in the UK between 1963 and 2018 is compared with the rate predicted by model (2) with the real GDP per capita published by the Maddison Project Database. The rate of unemployment is borrowed from the OECD. In the middle panel, the model residual errors are presented with the standard deviation of 0.91% and the mean unemployment rate of 5.99%. The lower panel depicts the linear regression of the measured and predicted time series with Rsq.=0.91. Hence, the new set of coefficients provides and an excellent match between the measured and predicted values, i.e. the model linking the change in the unemployment rate and the change in real GDP per capita. The UK is able to retain the integrity of data in longer time periods. The overall sensitivity of unemployment to real GDP per capita seems to decrease with time. This decrease is likely artificial as if one implicitly jumps from Fahrenheit to Celsius degrees and gets cooler weather.

Figure 2. Upper panel: The measured rate of unemployment in the UK between 1963 and 2018 and the rate predicted by model (2) with the real GDP per capita published by the MPD. Middle panel: The model residual, stdev=0.91% Lower panel: Linear regression of the measured and predicted time series. Rsq = 0.91. 

The link between unemployment and real economic growth in Spain

 

 

In our previous post, we revisited and validated the modified Okun’s law for Austria with new GDP and unemployment data for the years between 2010 and 2019. The revised model for Austria and other countries presented so far in this blog accurately describe the new data, i.e. the original model is validated. In order to reach the best fit between the measured and predicted unemployment rates, we introduce structural breaks related to the change in real GDP definition. To illustrate the breaks in the real GDP data (i.e. nominal GDP corrected for the price change) we compare price inflation estimates as defined by the GDP deflator and CPI. The latter is considered as a reference. It is also important that the goods and services in the CPI are also included in the GDP deflator, dGDP. In the past, the CPI and dGDP were almost equivalent and the deviation between them was forced by the introduction of such economic parameters as imputed rent in the 1970s. The experiments with the GDP deflator and nominal have been the essence of definitional activity ever since. The best is an enemy of good. Artificial breaks in the GDP and other economic variables make the work of researches very difficult.  

In this post, we revise the model for Spain and begin with the CPI and GDP deflator difference, which is used to reveal potential definitional breaks in the dGDP estimates. Obviously, such breaks in the dGDP create breaks in the real GDP per capita estimates, and thus, in the statistical estimates associated with our model. One has to find potential breaks and allow the model to compensate for corresponding disturbances and to provide unbiased statistical estimates of the defining parameters. There is another strong instrument in econometrics – dummy variables, which can explain the steps in many economic variables like labor force and unemployment. We do not use dummy variables in the model and achieve the best fit only with the structural breaks, i.e. the change in the coefficients of linear regression in the years of definitional revisions to the GDP deflator and nominal GDP. In that sense, our model becomes piecewise in order to match the changes in definition. It is like changing the speed from 105 km/h to 55 mph crossing the Canada/USA border, with physical speed not changing. 

In the upper panel of Figure 1, we present the evolution of the cumulative inflation (the sum of annual inflation estimates) as defined by the CPI and dGDP between 1970 and 2018 (the OECD data). Both variables are normalized to their respective values in 1970. The dGDP curve is close to the CPI before 1995 and then a significant deviation is observed. There is a low amplitude deviation between 1980 and 1990. After 1996, the deviation increases in amplitude, and the dGDP is first above the CPI curve and then dives below the CPI line in 2012.   In the middle panel, the rates of price inflation are shown for both indices. In the lower panel, we present the difference between the CPI and the dGDP cumulative inflation curves in the upper and middle panels. One can suggest the presence of breaks in 1979, 1985, 1995, 2007, and 2014. This is for the model to decide, however, when the breaks result in the bets LSQR fit.

      

Figure 1. Upper panel:  The evolution of the cumulative inflation (the sum of annual inflation estimates) as defined by the CPI and dGDP between 1970 and 2018. Both variables are normalized to their respective values in 1970.  Middle panel: The dGDP and CPI inflation estimates. Lower panel: The difference between the curves in the upper and middle panels. One can observe the breaks in the difference between the cumulative curves. We suggest potential breaks in 1979, 1985, 1995, 2007, and 2014.  

In a modified model for Spain, we are looking for breaks near the years presented in Figure 1 and obtain the following intervals and coefficients:

 

du_p = -0.40dlnG + 2.11,  1995>t≥1970

du_p = -0.95dlnG + 2.03,  1996≥t≥2013                              

du_p = -0.50dlnG  - 2.10,            t≥2014     (1) 

where du_p – one-year change in the (OECD) the unemployment rate, G – real GDP per capita (2011 prices). The break years in Figure 2 are close to those estimated from the inflation curves in Figure 1, but not all potential breaks are used. The overall fit shown in the upper panel is excellent, as confirmed by the residual errors in the middle panel and the regression (Rsq=0.96) of the predicted and measured employment between 1973 and 2018. We retain in mind that the estimates of the unemployment rate are obtained in the surveys. The unemployment values are also corrected in the revisions to the unemployment definition. 

Considering the fall in real GDP growth caused by the COVI-19 pandemic one could expect that the rate of unemployment in Spain may increase according to equation (1) and probably will stay at an elevated level. Coefficient -0.5 in (1) predicts that a 1% decrease in real GDP per capita is converted to a 0.5% increase in the rate of unemployment. For Spain with its extremely high historical unemployment rate, this is a big problem.  

Figure 2. Upper panel: The measured rate of unemployment in Spain between 1970 and 2018, and the rate predicted by model (1) with the real GDP per capita published by the MPD and the unemployment rate reported by the OECD. Middle panel: The model residual: stdev=1.3%. Lower panel: Linear regression of the measured and predicted time series. Rsq. = 0.96. 

The rate of unemployment in Austria will increase

 In our previous posts, we revisited and validated our version of Okun’s law for the USA, Canada, Germany, Australia, and France with new GDP and unemployment data the years between 2010 and 2019. In all five countries, the revised model accurately describes the new data, i.e. the original model is validated. In order to reach the best fit between the measured and predicted unemployment rates, we introduce structural breaks related to the change in real GDP definition. To illustrate the breaks in the real GDP data (i.e. nominal GDP corrected for the price change) we compare price inflation estimates as defined by the GDP deflator and CPI. The latter is considered as a reference. It is also important that the goods and services in the CPI are parts of the GDP deflator, dGDP. In the past, the CPI and dGDP were almost equivalent. 

In this post, we apply the same approach to Austria and start with the CPI and GDP deflator difference, which is used to reveal definitional breaks in the dGDP estimates. Obviously, such breaks in the dGDP create breaks in the real GDP per capita estimates, and thus, in the statistical estimates associated with our model. One needs to find such breaks and allow the model to compensate for corresponding disturbances.  In panel a) of Figure 1, we present the evolution of the cumulative inflation (the sum of annual inflation estimates) as defined by the CPI and dGDP between 1970 and 2018 (the OECD data). Both variables are normalized to their respective values in 1970. The dGDP curve is close to the CPI before 1982 and then some low-amplitude deviations are observed. After 1995, the deviation increases in amplitude and the dGDP is below the CPI curve – an indicator of good economic performance similar to that observed in Germany since 1996.   In panel b), the inflation rates are shown for both variables. In panel c), we present the difference between the CPI and the dGDP cumulative inflation curves in panels a) and b) and suggest the presence of breaks in 1982, 1996, and 2007. Panel d) of Figure 1 presents the original CPI curve and the corrected dGDP curve. The fit is good. As a result, our modified Okun’s law model is allowed to have breaks in 1982, 1996, and 2007.

 

a)        

b)

c)

d)

Figure 1. a) The evolution of the cumulative inflation (the sum of annual inflation estimates) as defined by the CPI and dGDP between 1970 and 2018. Both variables are normalized to their respective values in 1961.  b) The dGDP and CPI inflation estimates. d) The difference between the curves in panels a) and b). One can observe the breaks in the difference between cumulative curves. We propose the breaks in 1982, 1996 and, 2007.  d) The fit between the CPI and the dGDP cumulative inflation curves after correction of the latter in 1982, 1996 and, 2007.

 

In our model, we are looking for breaks near the years and obtain the following intervals and coefficients: 

du_p = -0.25dlnG + 0.60,  1982>t≥1970

du_p = -0.36dlnG + 0.97,  2006≥t≥1982                              

du_p = -0.40dlnG + 0.34,           t≥2007     (1) 

where du_p – one-year change in the (OECD) the unemployment rate, G – real GDP per capita (2011 prices). The break years in Figure 2 are the same as estimated from the inflation curves in Figure 1. The overall fit is shown in the upper panel in excellent, as confirmed by the residual errors in the middle panel and the regression (Rsq=0.92) of the predicted and measured employment between 1970 and 2018. We retain in mind that the estimates of the unemployment rate are obtained in the surveys. The unemployment values are also corrected in the revisions to the unemployment definition (e.g., Austria did not include in unemployment those who had no job before, i.e. graduates).

 Considering the fall in real GDP growth caused by the COVI-19 pandemic one could expect that the rate of unemployment in Austria may increase according to equation (1) and probably will stay at an elevated level. Coefficient -0.4 in (1) predicts that 1% of the drop in real GDP per capita growth is converted in a 0.4% increase in the rate of unemployment.

 

Figure 2. Upper panel: The measured rate of unemployment in Austria between 1970 and 2018, and the rate predicted by model (1) with the real GDP per capita published by the MPD and the unemployment rate reported by the OECD. Middle panel: The model residual: stdev=0.37%. Lower panel: Linear regression of the measured and predicted time series. Rsq. = 0.92.