Probability of the North Korea nuclear testing mountain collapse is increasing

Seismic stations in South Korea, China and Russia detected increasing aftershock activity within the mountain where the biggest DPRK nuclear test was conducted on September 3, 2017. A few aftershock events occur three months after the test and about two months after the previous aftershock on October 12. This activity might be related to a complete collapse of the mountain or chimney collapse with opening of a direct access from the explosion cavity filled with radioactive debris to the atmosphere. This is the worst case scenario, but one cannot exclude this effect especially in view of high international tension in this region. China, Russia and South Korea are just in tens of kilometers from the test site, and Japan is not too far away. Seismologists continue detailed study without interruption and delay. 

Did North Korea test tectonic weapon?

The Democratic People's Republic of Korea (DPRK) conducted 6 underground test, with  the last 5 from 6 in the same mountain. The biggest (sixth) event conducted on September 3, 2017 (DPRK6) had magnitude (mb=6) and resulted in visible landslides and also was followed by a few aftershocks with magnitudes between 2.4 and 3.4. Two of them occurred on September 23 (around 4:40 and 8:30 UTC) and are likely were reported today as the cause tunnel collapse and casualties. The test conducted on September 9, 2016 was also followed by a small aftershock well described in our paper (https://arxiv.org/abs/1611.03055 or https://link.springer.com/article/10.1134/S1028334X17030011 ), which is very similar to the aftershocks of the DPRK6.

Among many challenges in the analysis of various physical measurements related to the DPRK test is the absence of measurable levels of radioactivity after 4 from 6 events and just minor traces of radioactivity after the other two.  Very deep placement of warheads can prevent radioactive gas venting and  thus provide effective containment of radioactive debris,  Let's consider two possibilities alternative to nuclear testing. 

Having the last event equivalent to about 100,000 tons of TNT, one can reject the hypothesis that this DPRK test was a chemical blast. An alternative explanation would be testing of tectonic weapon, as introduced by Russian geophysicist V. Nikolayev in 1992. There are several physical mechanisms that can be used to facilitate effective release of pre-existing tectonic energy and generation of seismic waves. In any case, the mountain is exhausted after five tests and no more tectonic release is possible.  I do not believe that tectonic weapon can be as efficient as we observed in six DPRK events. In case it does exist this is an additional threat for the peaceful world. 

On decreasing rate of economic growth in the USA

We published a book and a few papers [e.g., 1, 2, 3] on economic growth. There are numerous plots demonstrating the decelerating rate of real economic growth in developed countries. The long-term component of real GDP increase is very simple – annual increment in real GDP per capita, rGDPpc, is a constant, which does not change with time since the late 1940s. This makes the rate of growth to be inversely proportional to the current level of rGDPpc: dln(rGDPpc(t)) = A/rGDPpc(t).  The population component is large in the USA – approximately 1% per year since the 1950s, but negative in Japan. Therefore, we use rGDPpc instead of rGDP, which is rather misleading.  

In the first paper published in 2005, we used data between 1950 and 2003 and found that the rGDPpc annual increment has a positive slope. It was considered as an indication of future problems between 2004 and 2016, which was actually observed as the Great Recession and further sluggish recovery. Figure 1 displays the original time series between 1947 and 2003 (black circles) extended by the estimated made since 2004. Instead of time, we use rGDPpc as argument. For recession periods, when the rGDPpc falls, one observes loops in the curve. Our prediction was right – the slope of the regression line has dropped from 0.02 (black linear trend line) to 0.007 (red trend line). The average increment in rGDPpc was $551 as measured  in 2009US$.  

Figure 1. Annual increment in real GDP per capita in the USA since 1947. Black circles show the period between 1947 and 2003, and red circles show the period extended into 2016. Linear trends are shown in black and red, respectively. Two regression equations demonstrate the change in slope since 2003.

There is another problem with the original rGDPpc time series borrowed from the BEA web site. It is calculated as a ratio of rGDP and total population, while only economically active population matters. Therefore, we have to correct the original  rGDPpc for the ratio shown in Figure 2. When the rGDPpc multiplied by the population correction factor, one obtains a better view on the long-term growth rate, as depicted in Figure 3. The trend in the rGDPpc annual increment is absent. This observation means that the rate of real GDP growth in the USA (and other developed countries) is decaying inversely proportional to rGDPpc. Leading economic countries will be growing at a rate of about 1.5% per year in the next 20 years. In 2016, the rate of rGDPpc growth was 0.9% per year. This mediocre growth will be accompanied by decreasing rate of population growth as observed since the earlier 2000s.

Figure 2. The ratio of total population and working age population (16 and above). The rGDPpc is corrected (multiplied) by this ratio.  

Figure 3. Same as in Figure 1, but for the population corrected rGDPpc.

Trump and real economy

I have no doubt that real economic growth in the USA  does not depend on presidential opinion and action. On average, the US will be growing at a rate of about 2% in the long run. In 2017, a recession is possible, but it has no connection to Trump. 

What Trump can change then? This is the question from both sides of the split nation. The complete answer is difficult to present in a post. I'll try to formulate some predictions, which are most relevant to my study.

1. Oil price depends on global processes and hardly to be corrected by Trump's energy policy. Hydrofracturing might become a cheaper technology because of lower expenses for environment protection.  

2. Most of commodities (iron, non-ferrous metals, grain, etc.)  are low now and will likely grow in the near future. It is good time to renew the US production capabilities. His plan to return production to the US is wise.

3. Competition with China is getting hot. China is a bigger US factory with cheap labor. All profit of cheaper labor is privatized by the top 1% of the US population. China cannot resist because its economy is not able to grow without the US market and investment. 

4. The richest 1%  is a big threat to Trump, however. Some of them are inside his administration. Bees against honey. Such conflicts are always resolved in favor of money, as Marx said. 

5. Inflation will be low with low volatility. US administration does not affect prices.

6. Stock market will fall in 2017.

7. Labor force participation will be falling and the labor force participation will not be growing  fast.

Recession may hit New Zealand in 2017, but at a five-year horizon real economic growth is about 2% per year

In 2010, we published a paper in the Journal of Applied Economic Sciences, which predicted real GDP per capita, rGDPpc, in several developed countries. Corresponding working paper was published in 2009 and covered the period before 2007. The evolution of rGDPpc in New Zealand was also presented in this blog in 2011.
Here, we revisit the 2010 model for New Zealand. It is important to stress that all defining parameters, which were estimated by the LSQ method from the data before 2008, are retained in the revisited model. Therefore, this is an out-of-sample test. The test result shows that our model accurately predicts the evolution of real GRP in New Zealand at an 8-year horizon. As predicted in 2009, in the next few years the growth rate will be increasing, except a deep fall in 2017, as we also expect in the USA.  Since the full prediction horizon is 14 years, we will be reporting on the model prediction in the future, but not often – the change in real economic growth is a slow process.
The original macroeconomic model for real GDP growth in developed countries was formulated in 2006 in the paper “GDP growth rate and population” published in the ECINEQ WPS. The model links the rate of growth in rGDPpc, g(t) = dln(rGDPpc)/dt, with the attained level of the rGDPpc and the rate of growth in population of a coutry-specific age.

g(t) = dln rGDPp(t)/dt  = A/rGDPpc(t) + 0.5dlnNs(t)/dt     (1)

 where empirical constant A and the specific age, Ns, are estimated from data. To obtain the model parameters, we used rGDPpc time series borrowed from the Total Economy Database.   The best fit annual increment value is A=$420 (notice that we used the EKS US$, as published by the Conference Board in 2016, while the GK 1990US$ were used before).  The term A/rGDPpc(t)  corresponds to inertial economic growth, which is observed when there is no change in the Ns.  The specific age population in New Zealand is 14 years, as in the previous versions. To describe the change in Ns, we used the age pyramid obtained in the 2006 census and extrapolated it in the past and in the future. The precision of Ns predictions decreases with the difference between the predicted year and 2006. We do not use fresher censuses because the goal of this study is to prove the model and to assess the accuracy of prediction at various time horizons. The largest time horizon for the 2006 census is 2021.
Figure 1 presents the observed and predicted GDP growth rates for New Zealand as obtained in 2008. Both curves are characterized by high-amplitude oscillations likely associated with measurement errors. Therefore, in Figure 2 we present both annual curves smoothed with MA(5) and MA(3), respectively. One can conclude that our prediction from 2008 was correct and real GDP per capita in New Zealand follows the predicted curve. This is the best validation of our model for NZ and the driving force of real economic growth in developed countries.

Figure 1. Observed and predicted growth rate of real GDP per capita in New Zealand between 1980 and 2015.  

Figure 2. The observed curve in Figure 1  is smoothed with a five-year moving average. The predicted rate is smoothed with MA(3). One can observe an outstanding accuracy of GDP prediction for 2009 and 2015 (between the smoothed curves).

GDP implicit price deflator in Germany will grow

In this blog, we introduced several models predicting inflation and unemployment in Germany in 2009 and 2010. These two posts presented a shorter version of our extended  paper published in 2007 on the dependence of the CPI, GDP deflator  (DGDP) and rate of unemployment, UE, on the change in labor force, LF. Two sources provide a complete description of our model and we are not going to repeat it in detail. Overall, the model says that one can describe inflation as a liner lagged function of the rate of labor force change, dLF/LF, and the rate of unemployment
DGDP(t) = adLF(t-6)/LF(t-6) + bUE(t-1) + c
where a, b, and c are empirical coefficients, t-6 means that dLF/LF leads inflation by 6 years, and t-1 means that UE leads DGDP by 1 year. Therefore, we have a one-year ahead natural prediction horizon. When we add new data, the empirical coefficients can change because the LSQ estimation procedure. But they should not change much.
Here, we revisit the DGDP prediction given 10 years ago using OECD data now available for the period between 2006 and 2016. Figure 1 compares the predicted and observed time series. Coefficients are as follows: a=0.3, b=-0.61, c=0.062, which are very close to the initial estimates in 2007. Overall, the observed curve is well matched by the predicted one, but the former has much larger variations. They disappear after smoothing with a four-year moving average, as shown in Figure 2. The fit is exciting. In Figure 3, we present the modelling error as the difference between the observed and predicted time series. This is an I(0) process, which is an important issue is the DGDP is a nonstationary process.
Conclusion: the GDP price deflator in Germany will be growing in the next years, despite the CPI inflation is close to zero. In 2016, DGDP was approximately 2%.

Figure 1. Predicted and measured DGDP in Germany

Figure 2. The observed time series is smoothed with 4-year moving average.

Figure 3. Modelling error is an I(0) process.

The French economy needs ”helicopter money” to boost labor force growth and avoid deflation

In 2013, 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. This update uses data for the past three years and proves that austerity is a counterproductive approach. We just extend inflation prediction by 3 years ahead (to 2019) and put new measurement without change in the previous estimates. We have nothing to add. The text is still valid.

“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 - 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. “