Economics as Classical Mechanics

The rate of unemployment in May below 7.5%?

2013-06-07T09:08:00.004+02:00

Today a new estimate will be announced for the rate of unemployment in the USA. We have been following this variable since 2006 when our general model linking labor force, price inflation, and rate of unemployment was first published. Approximately a month ago we predicted a significant drop in this rate in May 2013 as based on the fall in labor force five years ago (i.e. in 2008). The range of expert predictions for May 2013 in 7.4% to 7.5% as reported by the WSJ Market Data Center.

Our best guess is 7.3% to 7.1% as figure below suggests.

Waveform cross correlation for seismic monitoring of underground nuclear explosions. Part II: Synthetic master events

2013-05-17T07:54:00.003+02:00

A new working paper is available on arxiv.org This is a link to pdf file.

Abstract

Waveform cross correlation is an efficient tool for detection and characterization of seismic signals. The efficiency critically depends on the availability of master events. For the purposes of the Comprehensive Nuclear-Test-Ban Treaty, cross correlation can globally reduce the threshold monitoring by 0.3 to 0.4 magnitude units. In seismically active regions, the optimal choice of master events is straightforward. There are two approaches to populate the global grid in aseismic areas: the replication of real masters and synthetic seismograms calculated for seismic arrays of the International Monitoring System. Synthetic templates depend on the accuracy of shape and amplitude predictions controlled by focal depth and mechanism, source function, velocity structure and attenuation along the master/station path. As in Part I, we test three focal mechanisms (explosion, thrust fault, and actual Harvard CMT solution for one of the April 11, 2012 Sumatera aftershocks) and two velocity structures (ak135 and CRUST 2.0). Sixteen synthetic master events were distributed over a 1ox1o grid. We built five cross correlation standard event lists (XSEL) and compared detections and events with those built using the real and grand master events as well as with the Reviewed Event Bulletin of the International Data Centre. The XSELs built using the source of explosion and ak135 and the reverse fault with isotropic radiation pattern demonstrate the performance similar to that of the real and grand masters. Therefore, it is possible to cover all aseismic areas with synthetic masters without significant loss in seismic monitoring capabilities based on cross correlation.

Why Gavyn Davies is wrong about employment targeting

2013-05-13T12:03:00.001+02:00

Gavyn Davies criticises the Fed for wrong money policy targeting on unemployment rate, UER, instead of the rate of employment, E/P. He is right that the rate of unemployment does not express well the full capacity of the economy. However, the rate of employment is quite different from the UER. Two figures below do show that the UER has been varying around 5% to 7% since 1948. The Fed can claim that monetary policy is effective because UER is always back to 6%. How can they say that they control E/P if it really has a strong secular component. Gavyn puts the Fed in danger to be responsible for that they can not be responsible for. It will never happen if the Fed has a bit of sense. The rate of employment is out of control not only for the Federal Reserve but also for any authority.

A fourty-year period of energy price oscillation

2013-05-06T14:21:00.000+02:00

Figure 1. The difference between the headline and core CPI (both seasonally adjusted) since 1957. The V-shape suggests the similarity of the fall and rise paths. One can estimate the next peak extrapolating the current rise by the previous fall (between 1980 and 2000).

Figure 2. The difference between the headline and core CPI. The red curve is an inverse version of the blue curve reduced by -9 and shifted by 19 years ahead. One can estimate the distance between the peaks in 1981 and 2019 as the period of long term oscillations, which is approximately 40 years.

Figure 3. The difference between the energy and core CPI. The red curve is an inverse version of the blue curve reduced by -65. One can estimate the distance between the peaks as the period of long term oscillations. The energy price is likely on a downward trend already.

Employment in Canada revisited

2013-05-05T10:52:00.003+02:00

Two years ago we presented a parsimonious model describing the evolution of employment/population ratio in Canada. In essence, this model is a modified Okun’s law since there exists a trade-off between the change in unemployment and employment. Yesterday, we revisited the rate of unemployment in Canada based on a complimentary model and found an extraordinary high fit between predicted and observed curves. (A comprehensive description of both models also extended by examples in other developed countries is presented in our paper “Modeling Unemployment And Employment In Advanced Economies: Okun’S Law With A Structural Break”. )

Figure 1 compares the change in the rate of employment (the employment/population ratio), de, and the rate of unemployment, du, in Canada. As expected, the change in the rate of unemployment is slightly more volatile (also because of lower accuracy of measurements). We have retrieved all data on unemployment and employment from the U.S. Bureau of Labor Statistics.

Figure 1. The (negative) change in the rate of employment compared to the change in the rate of unemployment in Canada.

Two years ago we estimated several models of employment/population ratio, e. For Canada, the best-fit model has been obtained by the least-squares (applied to the cumulative sums):

de_t = 0.40dlnG_t – 0.67, t<1984
de_t = 0.44dlnG_t – 0.56, t>1983 (1)

where dlnG_t is the change rate of real GDP per capita at time t. Figure 2 shows the cumulative curves for the time series in (1). We did not fix the initial value in 1971 and obtained it from the regression. There is a structural break near 1984 which is expressed by a slight shift in the slope of the regression line and a 0.11 change in the intercept term. Since the latter term is cumulated over years one can consider this change as a significant one. It makes a 1.1% employment change over 10 years. The break is needed because of the change to definitions and measurement procedures rather than actual break in the long-run link between e and G. In any case, functional dependence between these two variables stays untouched.

The employment/population ratio varies between from ~54.5% in 1971 and ~64.1% (!) in 2008. The agreement between the actual and predicted curves in Figure 2 is excellent. We added two readings for 2011 and 2012 which practically coincide. This observation validates our original model and we will continue reporting on the evolution of employment/population ratio in Canada.

Figure 3 present results of a linear regression with R²=0.88 for the period between 1971 and 2012. The standard error of the model is 0.82% which is chiefly related to the first ten years where measurements were rather crude.

Figure 2. The cumulative curves for the observed and predicted change in the employment/population ratio, de.

Figure 3. Linear regression of the measured and predicted curves in Figure 2.

A fairy tale about the future rate of unemployment in the US

2013-05-04T21:51:00.001+02:00

The rate of unemployment, u, was very high (10%) in 2009. It has been often discussed that the fall in this rate is too slow historically. Figure 1 shows the evolution of u since 1980. There were two major peaks in 1982 (10.8%) and 1992 (7.8%). (All rates are seasonally adjusted.) When the troughs preceding the peaks are synchronized all three descending curves look very similar. This observation says that the current fall in u is not different from the previous. It also tells us a fairy story about the near future. This rate will fall into the second half of the 2010s. Meanwhile, it may fall to 6.0% in 2013 or in the first half of 2014.

It is worth noting that the start troughs before two major peaks in Figure 1 were higher than the end troughs. If this trend is retained the next trough should be ~4%.

Figure 1. The rate of unemployment in the US

Figure 2. Two previous major peaks synchronized with the most recent. The length of fall is approximately 7 years.

The beauty of science II. Predicting the rate of unemployment in Australia

2013-05-04T16:36:00.003+02:00

A few hours ago, I reportedthat Canada gives the best example of accurate quantitative prediction of unemployment in developed countries and therefore extreme satisfaction for a researcher. I changed my mind when revisited the case of Australia with new data - real GDP (GK per capita) data from the Total Economic Database and the rate of unemployment from the OECD. Two years ago, I presented a model based on data from the same sources and found relatively big discrepancy after 2000. In the new revision of the same data, this discrepancy have evaporated.

Historically, we published a paper in the Journal of Theoretical and Practical Research in Economic Fields in 2012. We presented the first version of the modified Okun’s law for developed countries including Australia. The model was estimated till 2010 and used the data available in 2011. Briefly, the model is estimated by the LSQ technique applied to the integral version of Okun’s law:

u(t) = u(t₀) + bln[G/G₀] + a(t-t₀) (1)

where u(t) is the predicted rate of unemployment at time t, G is the level of real GDP per capita, a and b are empirical coefficients. For Australia, we estimated the model with a structural break allowed by data somewhere between 1980 and 2000. The best-fit (dynamic) model minimizing the RMS error of the cumulative model (1) with the new data revision is as follows:

du = -0.69dlnG + 1.50, t before 1992

du = -0.45dlnG + 0.75, t after 1991 (2)

Originally, the model included the structural break neat 1995. With the new data the overall fit is better and the year of break moved to 1991, which is related to major revision of unemployment definition. The new model suggests a drop in slope and a big change in the intercept around 1991. Figure 1 depicts the observed and predicted curves of the unemployment rate. The agreement is very good. Figure 2 shows that when the observed time series is regressed against the predicted one, R²=0.86 (0.84 in 2011). The integral form of the dynamic Okun’s law (1) is characterized by a standard error of 0.72% for the period between 1971 and 2012. The average rate of unemployment for the same period is 6.9% with a standard deviation of the annual increment of 1.9%. This is an extremely accurate prediction considering the accuracy of GDP (~1% per year) and unemployment (0.3% to 0.4%) estimates. The whole discrepancy is related to the measurement errors and thus the residual error shown in Figure 3 is an I(0) random process.

The rate of unemployment depends on the cumulative change in real GDP per capita, as relationship (1) implies. To reduce the rate of unemployment in Australia, the rate of GDP (real per capita) growth must be above 1.7% per year.

Figure 1. The observed and predicted rate of unemployment in Australia between 1971 and 2012.

Figure 2. The measured time series is regressed against the predicted one. R²=0.86 with both time series likely to be stationary.

Figure 3. The residual error of the unemployment model.

So, I have to repeat. The beauty of science is the accuracy of prediction. It is difficult to express the feelings of a researcher than new observations fit his predictions based on a simple concept. It is especially sweet when this concept is different from the mainstream one.

The beauty of science. Unemployment in Canada

2013-05-04T10:49:00.001+02:00

The beauty of science is the accuracy of prediction. It is difficult to express the feelings of a researcher than new observations fit his predictions based on a simple concept. It is especially sweet when this concept is different from the mainstream one. I am sure that economists never feel like that with all models flawed. Here, I present one of many cases of accurate predictions based on the link between GDP and unemployment, which is a modified Okun’s law in an integral form.

Canada provides an excellent set of macroeconomic data which can be described by a few deterministic links with a high level of reliability and confidence. We have retrieved real GDP (GK per capita) data from the Total Economic Database and the rate of unemployment from the OECD. In 2012, we published a paper in the Journal of Theoretical and Practical Research in Economic Fields, where presented the first version of the modified Okun’s law for developed countries including Canada. The model was estimated till 2010 and used the data available in 2011. The original model for Canada was also presented in this blog in 2011. It’s time to revisit the model and its predictions.

Overall, the model is estimated using the LSQ technique to the integral version of Okun’s law:

u(t) = u(t₀) + bln[G/G₀] + a(t-t₀) (1)

where u(t) is the predicted rate of unemployment at time t, G is the level of real GDP per capita, a and b are empirical coefficients. For Canada, we estimated the model with a structural break allowed by data somewhere between 1980 and 1990. The best-fit (dynamic) model minimizing the RMS error of the cumulative model (1) is as follows:

du = -0.28dlnG + 1.16, t<1983
du = -0.28dlnG + 0.30, t>1982 (2)

This model suggests no shift in the slope and a bigger change in the intercept around 1983. Figure 1 depicts the observed and predicted curves of the unemployment rate. Considering the accuracy of measurements for both involved variable the fit is excellent. The integral form of the dynamic Okun’s law (1) is characterized by a standard error of 0.67% for the period between 1971 and 2012. The average rate of unemployment for the same period is 8.12% with a standard deviation of the annual increment of 0.92%. Figure 2 shows that when the observed time series is regressed against the predicted one, R²=0.87. Here we do not test both time series for stationarity but presume that the rate of unemployment has to be a stationary time series in the long run.
One can suggest that the rate of unemployment has been driven by real economic growth and there is no much room for other macroeconomic variable to intervene. Personally, I admire the performance of this simple model and will keep reporting on it for Canada, but also for Spain, France, etc.

Figure 1. The observed and predicted rate of unemployment in the Canada between 1970 and 2010.

Figure 2. The measured time series is regressed against the predicted one. R²=0.87 with both time series likely to be stationary.

CPI inflation in the UK will not fall below 2.5% to 3% per year till 2020

2013-05-04T09:45:00.003+02:00

Two years ago we presented a model for the rate of inflation in the UK and a prediction for 2010-2020. Actual rate in 2011 and 2012 was very close to the predicted one as Figure 1 demonstrates. There is no change to the model and we foresee another ten years of a reactively large (CPI) inflation rate in the UK – nearly 3% per year. In 2013, the predicted rate is 2.9%.

According to our concept, the original paper was published by the Euro Area Business Cycle Network, there exists a long-term equilibrium link between price inflation, CPI_t, unemployment, u_t, and the rate of change of labour force, l_t=dLF/LFdt. We follow up our predictions for many counties in this blog. The UK is one of the world biggest economies with a relatively good statistics started chiefly from 1973. It is a major challenge to model inflation in the UK using our approach.

There is a structural break in the link between three defining variables in 1985, which is purely artificial and induced by the change in measurement units and definitions. Accordingly, we have to distinguish two periods to fit observations: before and after 1985:

CPI_t = 1.0l_t + u_t - 0.046; t>1985

CPI_t = -1.0l_t -1.7 u_t + .025; t<1985 (1)

For both periods, inflation does not lag behind unemployment and l_t. Figure 1 presents the observed and predicted CPI curves, all variables were obtained from the OECD database in 2013. All in all, the predictive power of the model is good and timely fits major peaks and troughs. The change from negative to positive linear coefficient in 1985 needs a special explanation. But such effects were observed in other developed countries as well.

The NSO's labour force projection helps to predict the future inflation. Since the inflow of new employees is still positive, l_t >0, and the rate of unemployment does not foresees any dramatic decline in the long run one can be sure that inflation will be positive in the near future, as Figure 2 predicts.

Figure 1. The predicted and measured rate of consumer price inflation (CPI) in the UK.

Figure 2. The predicted rate of CPI inflation in the UK between 2008 and 2020 estimated from the labour force projection by the NSO.

The rate of participation in labor force in the U.S. will regain 0.7% in 2013-2014

2013-05-01T14:31:00.003+02:00

Five years ago we published a paper with a model describing the evolution of labor force participation rate, LFP, in developed countries. Among other countries, we presented a prediction for the U.S. We used the change in a younger population cohort and foresaw the fall by 1.5% in 2010. Figure 1 reproduces Figure 8 from the paper. This dramatic fall happened on time. It was a success of the model.

Figure 1. Prediction of the LFP evolution in the USA between 2000 and 2014 from the number of 3-year-olds. Flat segment between 2004 and 2009 will end up in a rapid drop by 1.3% after 2010. This is the effect of an elevated (above potential) real economic growth.

The predicted curve in Figure 2 was obtained from real GDP per capita as a proxy to the population change (see the article for details). Both curves in Figure 2 almost coincide between 1960 and 2012. The largest deviations are observed in the years of biggest revisions to the LFP after decennial censuses and changes to GDP definitions. Therefore, they can be neglected as having artificial character.

The model predicts the secular change in the LFP!

In 2011 and 2012, the rate of participation is expected to hover near 64.5%, but actually fell to 63.7%. As an option explaining the observed deviation, the rate of GDP growth in 2011 and 2012 could be overestimated. As an alternative, the rate of participation in labor force may rise by 0.7%. This is in line with the predicted fall in the rate of unemployment to 6% by the end of 2013.

Figure 2. Observed and predicted LFP in the U.S.

We accurately predicted the current rate of unemployment in Italy in 2008 !

2013-04-30T20:36:00.000+02:00

A year ago, we revisited our prediction of the rate of unemployment in Italy made in 2008and found that it was excellent. The model worked well. As it has an 11-year horizon, we can check our old prediction for 2012 and 2013 (preliminary). A new estimate of unemployment rate in 2012 is 10.6%. For 2013, the rate is 11.5 in March. There is no doubt, these values fully validate our model of unemployment as a function of the change in labour force.

We introduced the model of unemployment in Italy in 2008 with data available only for 2006. The rate of unemployment was near its bottom at the level of 6%. The model predicted a long-term growth in the rate unemployment to the level of 11% in 2013-2014.

The agreement between the measured and predicted unemployment estimates in Italy validates our concept which states that there exists a long-term equilibrium link between unemployment, u_t, and the rate of change of labour force, l_t=dLF/LFdt. Italy is a unique economy to validate this link because the time lag of unemployment behind l_t is eleven (!) years.

The estimation method is standard – we seek for the best overall fit between observed and predicted curves by the LSQR method. All in all, the best-fit equation is as follows:

u_t = 5.0l_t-11 + 0.07 (1)

As mentioned above, the lead of l_t is eleven years. This defines the rate of unemployment many years ahead of the current change in labour force.

Figure 1 presents the observed unemployment curve and that predicted using the rate of labour force change 11 years ago and equation (1). Since the estimates of labour force in Italy are very noisy we have smoothed the annual predicted curve with MA(5). All in all, the predictive power of the model is excellent and timely fits major peaks and troughs after 1988. The period between 2006 and 2013 was predicted almost exactly. (If anybody knows a better prediction in 2008 of 2013 unemployment rate please give us the link.) This is the best validation of the model – it has successfully described a major turn in the evolution of unemployment near its bottom. No other macroeconomic model is capable to describe such dramatic turns many years ahead. Four years ago, we expected the peak in the rate of unemployment in 2013-2014 at the level of 11% (+5% from the level in 2008) and it has come!

The evolution of the rate of unemployment in Italy is completely defined ten year ahead. Since the linear coefficient in (1) is positive one needs to reduce the growth in labour force (see Figure 3) in order to reduce unemployment in the 2020s. For the 2010s everything is predefined already and the rate of unemployment will be high, i.e. above 9%.

Figure 1. Observed and predicted rate of unemployment in Italy.

Figure 2. The rate of growth in labour force.

Waveform cross correlation at the International Data Centre: comparison with Reviewed Event Bulletin and regional catalogues

2013-04-27T17:04:00.001+02:00

Another poster at the EGU 2013.

Abstract

Waveform cross correlation substantially improves detection, phase association, and event building procedures at the International Data Centre (IDC) of the Comprehensive Nuclear-Test-Ban Treaty Organization. There were 50% to 100% events extra to the official Reviewed Event Bulletin (REB) were found in the aftershock sequences of small, middle size, and very big earthquakes. Several per cent of the events reported in the REB were not found with cross correlation even when all aftershocks were used as master events. These REB events are scrutinized in interactive analysis in order to reveal the reason of the cross correlation failure. As a corroborative method, we use detailed regional catalogues, which often include aftershocks with magnitudes between 2.0 and 3.0. Since the resolution of regional networks is by at least one unit of magnitude higher, the REB events missed from the relevant regional catalogues are considered as bogus. We compare events by origin time and location because the regional networks and the International Monitoring System are based on different sets of seismic stations and phase comparison is not possible. Three intracontinental sequences have been studied: after the March 20, 2008 earthquake in China (m_b(IDC)=5.4), the May 20, 2012 event in Italy (m_b(IDC)=5.3), and one earthquake (m_b(IDC)=5.6) in Virginia, USA (August 23, 2011). Overall, most of the events not found by cross correlation are missing from the relevant regional catalogues. At the same time, these catalogues confirm most of additional REB events found only by cross correlation. This observation supports all previous findings of the improved quality of events built by cross correlation.

Seismicity of the North Atlantic as measured by the International Data

2013-04-27T12:01:00.003+02:00

I have uploaded a posterpresented at the EGU 2013.

Abstract

The Technical Secretariat (TS) of the Comprehensive Nuclear Test-Ban Treaty Organization (CTBTO) will carry out the verification of the CTBT which obligates each State Party not to carry out nuclear explosions. The International Data Centre (IDC) receives, collects, processes, analyses, reports on and archives data from the International Monitoring System. The IDC is responsible for automatic and interactive processing of the International Monitoring System (IMS) data and for standard IDC products. The IDC is also required by the Treaty to progressively enhance its technical capabilities. In this study, we use waveform cross correlation as a technique to improve the detection capability and reliability of the seismic part of the IMS. In order to quantitatively estimate the gain obtained by cross correlation on the current sensitivity of automatic and interactive processing we compared seismic bulletins built for the North Atlantic (NA), which is a seismically isolated region with earthquakes concentrating around the Mid-Atlantic Ridge. This allows avoiding the spill-over of mislocated events between adjacent seismic regions and biases in the final bulletins: the Reviewed Event Bulletin (REB) issued by the IDC and the cross correlation Standard Event List (XSEL). To begin with, we cross correlated waveforms recorded at 18 IMS array stations from 1500 events reported in the REB between 2009 and 2011. The resulting cross correlation matrix revealed the best candidates for master events. We have selected 60 master events evenly distributed over the seismically active zone in the NA. High-quality signals (SNR>5.0) recorded by 10 most sensitive array stations were used as waveform templates. These templates are used for a continuous calculation of cross correlation coefficients in the first half of 2012. All detections obtained by cross-correlation are then used to build events according to the current IDC definition: at least three primary stations with accurate arrival times, azimuth and slowness estimates. The qualified event hypotheses populated the XSEL. In order to confirm the XSEL events not found in the REB, a portion of the newly built events was reviewed interactively by experienced analysts. The influence of all defining parameters (cross correlation coefficient threshold and SNR, fk-analysis, azimuth and slowness estimates, relative magnitude, etc.) on the final XSEL has been studied using the relevant frequency distributions for all detections vs. only for those which were associated with the XSEL events. These distributions are also station and master dependent. This allows estimating the thresholds for all defining parameters, which may be adjusted to balance the rate of missed events and false alarms.

Spain: 33% unemployed in 2013? Revisited

2013-04-25T18:18:00.002+02:00

Today, Bloomberg discussesthe rate of unemployment in Spain, which rose to 27.2%. This level is interpreted as an unexpectedly high. In October 2012, we described the evolution of unemployment in Spain(it was 25% on the date of posting) and predicted the rate of unemployment at 33% if real GDP falls by 10% in 2013. We are using the LSQ technique applied to the integral version of Okun’s law:

u(t) = u(t₀) + bln[G/G₀] + a(t-t₀) (1)

where u(t) is the rate of unemployment at time t, G is the level of real GDP per capita (we used TED, The Conference Board, EKS PPP ), aand b are empirical coefficients. The best-fit (dynamic) model for Spain minimizing the RMS error of the cumulative model (1) is as follows:

du = -0.406dlnG + 2.00, t<1995

du = -1.11dlnG + 1.54, t>1994 (2)

This model suggests a big shift in the slope and a smaller change in the intercept around 1995. Having a new unemployment estimate for 2012, we have updated the prediction of (2) in Figure 1. The 2012 real GDP reading fully confirms the excellent predictive power of the model. The predicted value is 25.5% and the reported rate of unemployment in the fourth quarter of 2013 is 26%.

Figure 1 also repeats our prediction (red circle) of the unemployment rate in Spain in 2013 for a 10% real GDP drop. The Bank of Spain report a 2% fall in Q1, which is much smaller than 10%. In any case the current economic performance in Spain is poor. From (2) it follows that even a zero GDP growth rate results in a 1.5% increase in unemployment.

Figure 1. The observed and predicted rate of unemployment in the Spain between 1971 and 2012. In 2013, the rate may reach 33% in case of 10% fall in real GDP.

The cumulative form of the dynamic Okun’s law is characterized by standard error of 1.68% for the period between 1971 and 2011 (0.92% after 1995). The average rate of unemployment for the same period is 13.6% (14.6% after 1995) with a standard deviation of the annual increment of 2.12%.

CPI deflation in Japan will strengthen

2013-04-18T14:16:00.003+02:00

I've borrowed this table from Japan Statistics. Considering the fact that only the price of fuel and transportation has been rising, one can interpret the current fall in oil price as a cause of the overall price drop in the months to come. Price deflation is strengthening.

Table 1 Japan, February 2013

	Index	Monthly Change(%)	Annual Change(%)
(10 Major Group Index)
All items	99.2	-0.2	-0.7
(All items, less imputed rent)	99.2	-0.2	-0.7
Food	99.3	-1.0	-1.8
Housing	99.2	0.0	-0.4
Fuel, light and water charges	108.7	0.1	3.0
Furniture and household utensils	89.9	0.7	-3.8
Clothes and footwear	95.5	-0.9	-0.5
Medical care	98.0	0.0	-0.5
Transportation and communication	101.9	0.6	0.9
Education	98.5	0.1	0.4
Culture and recreation	92.1	0.0	-2.8
Miscellaneous	103.4	0.2	-0.1
All items, less fresh food	99.2	0.1	-0.3
All items, less food (less alcoholic beverages) and energy	97.6	0.0	-0.9
(Goods and Service Group Index)
Goods	98.7	-0.3	-1.3
Services	99.6	0.0	-0.1

The rate of unemployment on its way to 6% in December 2013

2013-04-16T11:28:00.003+02:00

Four months ago, we discussed the rate of unemployment in the US and published our forecast for 2013. We predicted an extended unemployment fall period down to the level of 6.2% in the third quarter of 2013. This prediction was made after we accurately forecasted (on March 1, 2012) the rate of unemployment in the US to fall down to 7.8% by the end of 2012. Here we update our model and present the evolution of the unemployment rate in the first quarter of 2013. The measured rate has been following our prediction up. We foresee the rate to fall down to 6% in the fourth quarter of 2013.

In 2006, we developed three individual empirical relationships between the rate of unemployment, u(t), price inflation, p(t), and the change rate of labour force, LF(t), in the United States. We also revealed a general relationship balancing all three variables. Since measurement (including definition) errors in all three variables are independent it may so happen that they cancel each other (destructive interference) and the general relationship might have better statistical properties than the individual ones. For the USA, the best fit model for annual estimates was a follows:

u(t) = p(t-2.5) + 2.5dLF(t-5)/dtLF(t-5) + 0.0585 (1)

where inflation (CPI) leads unemployment by 2.5 years (30 months) and the change in labor force leads by 5 years (60 months). We have already postedon the performance of this model several times.

For the model in this post, we use monthly estimates of the headline CPI, u, and labor force, all reported by the US Bureau of Labor Statistics. The time lags are the same as in (1) but coefficients are different since we use month to month-a-year-ago rates of growth. We have also allowed for changing inflation coefficient. The best fit models for the period after 1978 are as follows:

u(t) = 0.63p(t-2.5) + 2.0dLF(t-5)/dtLF(t-5) + 0.07; between 1978 and 2003

u(t) = 0.90p(t-2.5) + 4.0dLF(t-5)/dtLF(t-5) + 0.30; after 2003

There is a structural break in 2003 which is needed to fit the predictions and observations in Figure 1. Due to strong fluctuations in monthly estimates of labor force and CPI we smoothed the predicted curve with MA(24).

The structural break in 2003 may be associated with the change of sensitivity of the rate of unemployment to the change of inflation and labor force. Alternatively, definitions of all three (or two) variables were revised around 2003, which is the year when new population controls were introduced by the BLS. The Census Bureau also reports major revisions to the Current Population Survey, where the estimates of labor force and unemployment are taken from. Therefore, the reason behind the change in coefficients night be of artificial character - the change in measuring units.

Figure 1 depicts the prediction and the observed fall in the rate of unemployment. Figure 2 shows that the observed and predicted time series are well correlated (R²=0.82). This is a good statistical support to the model.

Figure 3 depicts the predicted rate of unemployment for the next 12 months. The model shows that the rate will fall to 6.0 % by December 2013. For 110 observations since 2003, the modelling error is 0.4% with the precision of unemployment rate measurement of 0.2% (Census Bureau estimates in Technical Paper 66). Hence, one may expect 6.0% [±0.4%]. Meanwhile, we expect a dramatic drop in the rate of unemployment in April/June 2013. It should come as “unexpected” by the mainstream economic forecasters.

Figure 1. Observed and predicted rate of unemployment in the USA as obtained in April 2013.

Figure 2. Observed vs. predicted rate of unemployment between 1967 and March 2013. The coefficient of determination Rsq=0.82.

Figures 3. The predicted rate of unemployment. We expect the rate to fall down to 6.0% in December 2013.

An unusual feature of income distribution in the USA - a Pareto law at very low incomes

2013-04-15T13:33:00.004+02:00

There is an invaluable source of quantitative information on personal income distribution (PID) in the USA which is hidden in a set of clumsy photocopies one can find searching the most remote parts of the Census Bureau web site. Today, we are discussing the distribution of personal money incomes in the age group 14 to 15 years. This is an extremely narrow group with data available only between 1967 and 1974. The essence of this short time series consists in the power (Pareto) law distribution of personal incomes starting from zero level. It is a well-known fact that the highest incomes are controlled by the Pareto law as well as many distributions in physics associated with self-organized criticality. Therefore, we consider the 14 and 15 years PID as an extremely important quantitative feature likely related to the frozen structure of income distribution not to the long-term process. These people just entered the economy and were distributed over the pre-existing structure straight away.

Some words about the data. I am a long-term user of the CB data but it took me couple minutes to get through a broken link to the directory http://www.census.gov/prod/www/population.html, where the original copies sit. They are not digitized. Fortunately, I did my homework in 2003 and spent a month to convert them into a digital format. These data allowed developing a mechanical model for the evolution of personal incomes since 1947, which is presented in my book “Mechanical model of personal income distribution”. There is much more left in the dataset, however.

Figure 1 below displays four personal income distributions for 1968. We present three age groups: 14 to 15, 16 to 19, and 20 to 24 years as well as the whole population distribution marked “all”. In the group 14 to 15 years, there exists a power law distribution with an exponent of -2.01 for the whole income range. In the group between 20 and 24 and in “all” only higher incomes (>$5000) are distributed according to the Pareto law. The PID for the group 16 to 19 is rather exponential. Hence, the PID evolves over time. It starts from a power law for very young people and then transforms into an exponential distribution for work experience of a few years. After five to seven years of work, two branches are observed: a quasi-exponential PID at lower incomes and a power-law one for higher incomes. It is important to stress that the PID for the Pareto law for higher incomes is characterized by a different exponent of -3.2 (see Figure 2). This is a quite different distribution.

By all means, the PID evolution expresses both the underlying structure of income distribution and the long-term process. It is highly unfortunate that the Census Bureau reported so short time series for younger people and did not provide reliable estimates of the Pareto distribution for higher incomes – only three to four estimates covering the range of high incomes.

Figure 1. Four PIDs in 1968. Notice the power law distributions for the whole income range in the group 14 to 15 as well as for higher incomes (>$5000) in the group between 20 and 24 and in “all”. The PID for the group 16 to 19 israther exponential.

Figure 2. The estimation of two exponents for power law PIDs.

Money income inequality in the USA has not been changing since 1947 in all age groups

2013-04-07T11:17:00.001+02:00

The Census Bureau reports money income distribution measured in the annual Current Population Surveys. Figure 1 shows the portion of population with income in various age groups. From 1947 to 1977, the portion with income grows linearly. In 1947, only 64.4% of the working age population in the USA had incomes according to the CB definition. In 1990, the portion peaked at 93.3%. The group between 15 and 24 years of age has a limited time span and demonstrates a dramatic fall since 1994. In other age groups, the portion also falls since 1994, but at a slower rate. In 1994, a new income definition and measuring procedure was introduced. The CB reports on the CPS results only from 1994. Before, only scanned copies of paper reports were available.

The estimates of money income inequality depend on income definition and thus on the portion of population with income. Figure 2 depicts the evolution of Gini ratio in the same age groups, which reveals a similar growth between 1947 and 1977. When the change in the portion of population with income is compensated in the past, the Gini ratios in Figure 2 are likely to be constant since 1947.

The level of (money) income inequality in various age groups is practically constant over time.

It is worth noting that the jump in the portions of population with income between 1977 and 1979 did affect the Gini estimates and a step of 0.02 is observed in all groups above 25 years of age. The youngest group and the overall estimate did not show any change.

Figure 1. The portion of population with income in various age groups as reported by the Census Bureau.

Figure 2. Age dependent Gini ratio since 1947 as measured from personal income distributions published by the Census Bureau. “With income” – the Gini ratio for the whole working age population with income.

Money income inequality in the USA is rock solid

2013-04-06T21:31:00.000+02:00

Ten years ago we started modelling income inequality in the US using data gathered by the Census Bureau. Personal money income has been measured in the annual Current Population Surveys (CPS) since 1947. The CB counts populations in various income bins and publishes the distribution of personal incomes. Unfortunately for researches, only PDF scans are available before 1994. When these scans were still available, we downloaded them and converted into digital format. These distributions allow direct calculations of Gini ratio. Figure 1 demonstrates that the level of inequality, as expressed by money income, is rock solid since the late 1950s. It oscillates between 0.51 and 0.52. This is the longest and most consistent time series of income inequality.

Between 1947 and 1959, Gini ratio grew.

Figure 1. Gini ratio since 1947

The use of synthetic master events for waveform cross correlation

2013-04-05T18:48:00.003+02:00

The EGU General Assembly 2013 starts on Monday, April 8. We have three posters on various aspects of waveform cross correlation in seismic monitoring. This is the link to one of them in PDF. www.academy.edu is again very useful.

And abstract:

The use of synthetic master events for waveform cross correlation

Abstract

It has been clearly demonstrated that waveform cross correlation substantially improves signal detection, phase association and event building. These processes are inherently related to the Comprehensive Nuclear-Test-Ban Treaty (CTBT) monitoring. The workhorse of cross correlation is the set of seismic master events (earthquakes or explosions) with high quality waveform templates recorded at array stations of the International Monitoring System (IMS). For the monitoring to be globally uniform, these master events have to be evenly distributed and their template waveforms should be representative and pure. However, global seismicity is characterized by a non-uniform distribution. Therefore, the master events selected from the Reviewed Event Bulletin (REB) produced by the International Data Centre (IDC) can be found in the areas constrained by the global seismicity. There are two principal possibilities to populate the globe with master events: to replicate real REB events or to build synthetic events. Here we compare the performance of these two approaches as applied to the aftershock sequence of the April 11, 2012 Sumatera earthquake. To compute synthetic waveforms, we use AK135 teleseismic velocity model and local CRUST-2 models for source and receiver, and four different source functions representing three different source mechanisms for earthquakes and one for explosion. The synthetic modelling is performed for teleseismic events and based on the stationary phase approximation to a wave equation solution developed by J. Hudson. The grid covering the aftershock area consists of 16 points. For each grid point, we find detections associated with real, replicated, and four versions of synthetic master events at seven IMS array stations, and then build event hypothesis using the Local Association (LA) procedure based on the clustering of origin times as estimated by back projection of the relevant arrival times with known master/station travel times. Then all conflicts between the hypotheses built by different masters for physically same events are resolved. There are two principal ways to compare the performance of actual, replicated, and synthetic master events: to compare the characteristics/distributions of detections (also station dependent) and those of event hypotheses. Both datasets have shown that the synthetic events provide the same overall performance as the real and replicated master events. The best performance is associated with the explosion source and the earthquake with the Harvard CMT solution for one of real events. When source mechanism and velocity model are appropriately chosen, the global grid of synthetic masters may allow a reduction in the magnitude threshold of seismic monitoring and improving the accuracy and uncertainty of event locations at the IDC to the level of the best located events. When a ground truth event is available, one can expand its influence over hundreds of kilometres.

Time to sell the S&P 500

2013-03-19T20:18:00.002+01:00

In the middle of December 2012, we reportedon the evolution of S&P 500 and decided to sell the index at 1430, which we had bought at 1352 in November. Due to technical reasons not related to our investing decisions we were able to sell the whole lot only on December 15 at 1420. The return from November is 4.7%; and from May 2012, when we first formulated this tactics, we made around 15%. We also decided to buy the S&P 500 at ~1400 whenever it comes. This level was touched on December 28. Then we predicted a rally to 1550 according to our simple and straightforward investing tactics – we follow the trajectory of the S&P 500 observed during the rally between 2002 and 2007. In March 2013, the S&P 500 exceeded 1560 and currently stays to fall to 1450 in a month. If to follow up our procedure, as explained in Figures 1 through 6, it’s time to sell and wait for a new short-term rally in June-October 2013. Then a tremendous fall down to 750 in 2015 is not excluded. Since 2009, the S&P 500 has been exactly following the previous trajectory. Why should it stay high after October 2013? Currently, I am in 10-year bonds and look for 1.5% to 1.7% yield to sell them in June.

In March 2012, we first publisheda graph which showed that the S&P 500 index would have a local fall in May 2012 at the level 1300. In a few sessions, we bought S&P 500 index in May/June 2012 at the level 1287 to 1322. The initial idea was to sell by the end of 2013 at 1525 and get a 12% to 14% return. In September, the S&P was at 1450, which was far above the expected level, and we decided to sell and wait a negative correction to 1350 to 1375 to re-enter the index. Selling at ~1460, we obtained an approximately 10% return in September. In October, the S&P 500 fell to 1350 as had been predicted in September and we re-entered in two sessions at 1348 and 1355. By the end of November the S&P 500 regained 4.5% (1416). Two weeks ago, we expected the index to rise to 1430 to 1450 in December 2012. This was considered as the best time to sell before the next negative correction in December 2012/ January 2013. In reality, this correction to 1400 was very short.

Below we present the evolution of the S&P 500 and the step-by-step assumptions illustrating the decisions we have made since March 2012.

Figure 1 shows the evolution of the S&P 500 index since 1980. After 1995, the index behavior reveals some saw teeth with peaks in 2000 and 2007. The current growth resembles those between 1997 and 2000 and from 2003 and 2007. There are two deep troughs in 2002 and 2009 which are marked by red and green lines. For the current analysis we assume that the repeated shape of the teeth is likely induced by a degree of similarity in the evolution of macroeconomic variables. The intuition behind such an assumption is obvious – in the long run the stock market depends on the overall economic growth.

Having two peaks and troughs between 1995 and 2009, what can we say about the current growth in the S&P 500? Before making any statistical estimates, in Figure 2 we have shifted forward the original curve in Figure 1 in order to match the 2009 trough (blue line). When the 2002 and 2009 troughs are matched, one can see that the current growth path closely repeats that after 2002. The first big deviation from the blues curve in Figure 2 started in 2011 and had amplitude of 150 units (from 1210 to 1360). The black curve returned to the blue one in August/September 2011. From December 2011, we observed a middle-size deviation of about 100 units.

Figure 1. The evolution of the S&P 500 market index between 1980 and 2012.

In April 2012, we predicteda drop in the S&P 500 to the level of 1300 by the end of May. Figure 2 shows the predicted behavior in April and May 2012, with the predicted segment shown by red line. We expected that the path observed in the previous rally would be repeated with the bottom points coinciding. When this prediction realized, we invested at the average price 1320. In May 2012, the expected exit level was 1500 in October 2013.

Figure 2. The original S&P 500 curve (black line) and that shifted forward to match the 2009 trough (blue line). Red line – expected fall in the S&P 500: from 1400 in March to 1300 in May.

Figure 3 shows the evolution of the S&P 500 monthly closing price between May and August 2012. The S&P 500 closing level for August was 1430 and reached 1469 in the middle of September. This level provided a ten percent return over approximately 4 months. One can see that the observed level was far above the expected level (blue line). The return and the deviation from the expected level both made us think that this was the best time to exit. We sold the index on September 21 (1460) anticipating strong turbulence (economic, financial, and political) and an overall fall to 1375 at a few month horizon.

Figure 3. Same as in Figure 2 with an extension between May and August.

Figure 4 shows the evolution of the S&P 500 monthly closing price in September-November 2012. The October’s closing level was 1411. On October 26, we put the November’s level down to 1375. One can see that the red line intersects the blue curve. The previous history of the black and red lines intersection with the blue one made us think that the time to enter the market (S&P 500 index) was approaching. We expected to buy at 1350 to 1375.

Figure 4. Same as in Figure 2 with an extension between September and November 2012.

Figure 5 depicts the state of the S&P 500 on December 15, 2012. The index had a local minimum of 1347 in the middle of November and recovered to 1416 on November 30. This was 15 points less than the expected level of 1430 in December 2012. The red line intersects the blue one in January 2013 and a negative correction to 1400 (or less) was expected.

Figure 5. Same as in Figure 4 with an extension into December 2012.

Figure 6 displays the current situation with the S&P 500. As in a few cases after 2010, the actual index (red line) leads the expected one (blue) by a month or so. It has reached the (closing) level 1563 on March 14 and has been falling since then. As in April and November 2012, we expect the index to fall. The level is likely around 1455. Then it might regain its power and have a final spurt to 1560 in October 2013. Do not miss this last opportunity.

Figure 6. Same as in Figure 5 with an extension into March 2013.

The quality of the Census Bureau's statistics is highly disappointing

2013-03-19T16:13:00.003+01:00

Looking closer at the household income distribution reported by the Census Bureau I found an extremely confusing conflict of data on Gini ratio. There are two tables providing annual Gini ratio estimates for all households: “HINC-01. Selected Characteristics of Households, by Total Money” and “Table H-4. Gini Ratios for Households, by Race and Hispanic Origin of Householder: 1967 to 2011”. The former estimates are available since 1998 in “Detailed Tables”. Figure 1 reveals a dramatic difference between these two estimates for the same years. The HINC-01 time series does not show any Gini ratio increase between 1998 and 2008 unlike the H-4 series.

There is a dramatic 0.02 step in 2009 in the HINC-01 series, which is actually fully related to the change in bin counting in 2009: the original $2500 bins between $0 and $100,000 were replaced by $5000 bins between $0 and $200,000. The change in the range and granularity of data resulted in the observed Gini ratio shift. Figure 2 displays the Lorenz curves for the years between 1994 and 2011 as obtained from the household income distributions in the relevant HINC-01 tables. The Gini ratio step of 0.02 is explained by the difference between the 2008 and 2009 Lorenz curves. Therefore, it is of artificial character and the HINC-01 curve would be at the level of 0.45 between 2009 and 2011 if to retain the $2500 bins and $100,000 range.

We have recalculated the Gini ratio for all years between 1994 and 2011 from the original household income distribution and found that the calculated curve is in excellent agreement with the HINC-01 one between 2005 and 2011 and coincides with the HINC-4 curve from 1994 to 1996.

These observation are extremely confusing and likely manifest internal changes in the CB procedures of Gini ratio estimation and publication. We cannot understand why formally identical time series differ so much and why the HINC-4 curve demonstrates a growth tendency while the underlying data do not show any reason for that. This difference has also many political implications since the research community, officials, media, and the blogosphere all discuss the growth in household Gini, which is not the case.

Figure 1. Gini ratios from HINC-01 (detailed) and HINC-4 (historical) tables.

Figure 2. The evolution of the Lorenz curves for the household income distribution between 1994 and 2011 in the USA. In 2009, the bin counting was changed from $2500 in the range to $100,000 to $5000 with the upper bin between $195,000 and $200,000.

Waveform cross correlation for seismic monitoring of underground nuclear explosions. Part I: Grand master events

2013-02-27T15:27:00.001+01:00

A new paper is now available via arxiv.org:

Abstract

Seismic monitoring of the Comprehensive Nuclear-Test-Ban Treaty using waveform cross correlation requires a uniform coverage of the globe with master events well recorded at array stations of the International Monitoring System. The essence of cross correlation as a monitoring tool consists in a continuous comparison of digital waveforms at a given station with waveform templates from the global set of master events. At array stations, cross correlation demonstrates a higher resolution because the time delays at individual sensors from master and slave events are the same but they may differ from theoretical ones used in standard beamforming. In the regions where master events and thus waveform templates are available, one can reduce the amplitude threshold of signal detection by a factor of 2 to 3 relative to standard beamforming and STA/LTA detector used at the International Data Centre. The gain in sensitivity corresponds to a body wave magnitude reduction by 0.3 to 0.4 units and doubles the number of detected events. This gain is crucial for seismic monitoring under the CTBT. The coverage by real master events is sparse and confined to areas with historical seismicity, however. In two parts of this study, we investigate the possibility to populate the global grid with real and synthetic master events. In Part I, we replicate a high-quality master event over a regular grid several hundred kilometers from its actual position. In Part II, we model waveform templates using synthetic seismograms with the aim to apply them in aseismic zones. Both approaches are tested using the aftershock sequence of the April 11, 2012 Sumatera earthquake (Ms(IDC)=8.2). We used sixteen master events to recover the aftershocks in the Reviewed Event Bulletin of the IDC.

PDF copy of the paper

Re-post

2013-02-27T14:46:00.001+01:00

starshinazapasa

Журнал Аркадия Бабченко

Законопроект "Об оскорблении чувств младших научных сотрудников"

: starshinazapasa; February 27th, 10:03

Как человек, верующий исключительно в науку, начал разрабатывать законопроект об оскорблении чувств доцентов, лаборантов и прочих ученых мужей. Стану депутатом - внесу в Думу.

За оскорбление "научных чувств ученых" - от трех до пяти. Формулировка специально будет максимально расплывчатой и неконкретной, чтобы можно было посадить любого. Ляпнул, что дважды два пять - и на двушечку без скощухи. Будьте любезны, присядьте пожалуйста.
За танцы и пение на кафедре (особенно в балаклавах) - два года.
Книги, тетради, учебники, карандаши, ай-пады и пр. и пр. можно будет покупать только в университетской лавке. Учебные пособия, купленные вне университетской лавки, будут считаться антинаучными и знания не принесут.
Российская Академия Наук получит право беспошлинной торговли водкой и сигаретами.
В школах и детских садах будет введено изучение теории Большого Взрыва.
Президент Академии Наук будет ездить на двух "Кадиллаках", которые загоняют в специальный железнодорожный вагон. Охранять его будут сотрудники ФСО. Один, специально приставленный, перед посадкой в автомобиль будет складывать знак Интеграла у него на профессорской шапочке.
Президент Академии Наук будет ездить с мигалкой.

В каждой деревне будет построено по академии или как минимум университету шаговой доступности. Детские сады, библиотеки, общественная собственность - будут отчуждены в их пользу. Жители Валаама будут выселены повторно. Так, чисто за компанию.
Раз в год, в день Большого Взрыва, все вещество, над которым любой лаборант или МНС прочитает теорему Ферма - становится антивеществом. Из-за чего приобретает лечебные свойства, а также не будет тухнуть на протяжении всего остального года.
Здание Академии Наук будет находиться на балансе города. Но внутреннее пространство будет принадлежать Московской Профессуре. Автомойка, банкетные залы, торговые точки - также будут принадлежать Московской Профессуре и не будут облагаться налогом. Потому как будут производить не торговлю, а дарение в обмен на дензнаки по строго установленной таксе.
Засорение нанопылью старых научных фолиантов карается штрафом в 20 млн. рублей.
Появление Президента Академии Наук на публике без часов "Брегетт" не допускается.
В армии будет введен институт Военных Научных Сотрудников. В каждой части будет построено по университету.
Оскорбление в блогах Президента Академии Наук будет преследоваться по закону. ФСБ будет предложено "более дерзко" расследовать такие преступления.
Мы уйдем от вульгарного примитивного понимания креационизма.
Ученых тыкать в каждую телепередачу по поводу и без повода.
За отрицание теории Дарвина будет введена уголовная ответственность.
При попадании даже самых младших научных сотрудников все видеозаписи ДТП мгновенно аннигилируются.
На Московскую же Профессуру с мигалками действие ПДД, а также административного и уголовного кодексов - не распространяется.
Если какой-нибудь председатель ОЗПП Михаил Аншаков вздумает задаться вопросом, почему наше дарение в обмен на строго фиксированную таксу в Храме Науки не является торговлей, а также почему автомойки и прибыль - наша, а ремонт и поддержание здания - на балансе города, бить его по башке монтировкой нещадно, обвинять в клевете и заводить уголовные дела

Re-published from oilprice.com

2013-02-21T08:17:00.001+01:00

The End Game for Oil & Gas Juniors: Interview with Chris Cooper

By James Stafford | Wed, 13 February 2013 23:19 | 0

The oil and gas game can be a tricky one for junior companies, but if played right the pay-off can be massive. At a time when juniors are risking a lot in volatile venues in the Middle East and Africa, Canada’s Aroway Energy (ARW) is planting its feet firmly in homeland soil and in conventional plays.
Why? Because for the smaller juniors this is not a long-term game and blowing all your capital to drill a single unconventional well in a risky frontier won’t pay off. Canada still has plenty to offer for juniors, even though you have to kiss plenty of frogs to find the prince. The end game, after all, is merger and acquisition.

In an exclusive interview with Oilprice.com, Aroway CEO Chris Cooper discusses:
• How to make or break a junior oil and gas company
• Why rail is becoming more attractive than pipeline transit
• Why most juniors won’t make it big in risky frontiers
• Why Keystone XL will get the green light
• Why oil and gas prices will increase
• Why the smaller juniors will stick to the conventional plays
• How the asset market is heating up … and what is ideal
• Why having control of infrastructure is key to success
• Where Canada’s oil and gas industry will be in a decade
• What every junior’s goal should be
Interview by James Stafford of Oilprice.com
James Stafford: Junior oil companies have been storming the scene with some bold investments in tricky frontier areas. Where do you see this going and what will the next phase for the juniors be? Where will the action be, in conventional or unconventional plays?
Chris Cooper: I am a big believer in the conventional plays. I find that the non-conventional plays are turning into more of a game for the intermediate-size companies primarily as a result of the capital that is required to exploit the resources. Horizontal wells with multi-stage fracturing is an expensive game. I find that the conventional plays expose juniors to a less risky scenario with higher returns on investment and longer-term production more often than not.
Given the current state of the capital markets and the scarcity of funding, I think the smaller juniors will continue to play in the conventional arena.
James Stafford: What’s the ideal partner for a junior company, and what can make or break it for a junior?
Chris Cooper: As far as make or breaking a junior, I believe you need to minimize the company’s risk by drilling wells that are going to give you good internal rates of return and steady production; a good mix of development and exploration wells. It is also very good to have a good operator that is responsible in keeping a control on costs.
James Stafford: What separates the good management teams from the mediocre in the Canadian junior oilpatch?
Chris Cooper: Management teams that have built and sold companies in the past have a responsible, methodical approach to how they run their businesses. More often than not, these teams do not try to re-invent themselves by drilling wells and formations that they have not done in the past. They continue to focus on what they know best, whether it be drilling in the Peace River Arch, chasing Leduc wells, or focusing on cardium wells. They often do not stray from their formulas and that is why they are good teams.
James Stafford: More Canadian oil is now being marketed by rail. Can you put the rail versus pipeline transport comparisons into perspective for us from a Canadian operating perspective?
Chris Cooper: A lot of companies, including Aroway, are capitalizing on the benefits of moving their oil via rail as opposed to pipeline. I think it will increase our netback, our profit per barrel, by several dollars immediately.
For instance, before we purchased our West Hazel Property in Skaskatchewan, the owners would truck to Talisman or another big operator that was pipeline-connected. Then, once the oil got to the pipeline-connected operator, they had to pay a certain amount of money to get it in the pipeline for diluents to meet pipeline specifications. Then they had to pay for the pipeline tariff and then they got the price the pipeline operator provided wherever they were on the pipeline.
So for example, the last month we got $53 to $54 a barrel, after the blend-in tariff for our West Hazel production, which is probably the lowest you’re going to see for a long time. Our netback on that oil was still greater than $20 a barrel. But for that $53.32 a barrel we sold, if transported by rail, we remove the pipeline tariff, we remove the blend for the diluents and we get $9 more added to the netback value.
So what we’ll end up doing is trucking our oil from the field to a company called Altex Energy, which is partly owned by Shell Canada. Shell owns all the railway cars and all these railway cars get filled up with heavy crude and shipped down to their Port Arthur facility on the Gulf Coast. At Port Arthur what typically happens to our crude--because it’s somewhere between 11 and 13 degree oil—is it goes straight into bunker fuel for ships.
So the refinery doesn’t have to touch it in some cases and that’s where we get a pretty substantial bump. Then you’re not subject to pipeline apportion and issues. It just opens up whole new markets for you.

At the end of the day we will get somewhere around $66 or $63 a barrel this month and then we’re going to bump that up by another $9 next month by taking all the crude we have in West Hazel by rail. So our netback will be $35 to $40--and that’s just the West Hazel crude.
James Stafford: What is the market like for assets right now, from a junior’s perspective? What’s the ideal prospect?
Chris Cooper: Asset sales are heating up. We are finding that there are a lot of assets being marketed through companies like Sayer and NRG Divestments. There are also several larger brokerage firms representing companies for “strategic alternatives.”
As an example, Aroway just picked up a great producing asset in Saskatchewan for $10,000/flowing barrel. The market for similar assets in Saskatchewan at that time was about $40,000/flowing barrel. Companies need to exercise patience and do their due diligence. Not to mention kissing a lot of frogs to find these types of assets. They are out there.

James Stafford: A lot of North American juniors are hitting the riskier frontiers with all they’ve got these days—from Iraqi Kurdistan to Sudan, even Somalia. Why are they willing to take this risk and is it paying off?
Chris Cooper: With higher risk comes higher reward, but I don’t think it is paying off in the broader sense. Sure, there are 2 or 3 juniors that have hit home runs, but more often than not a junior is going into those types of plays with only $5 or $10 million in the treasury and they blow this after drilling just one well. I have always believed there is great opportunity offshore, but the risks are lower and infrastructure and political stability in North America is in place. There is plenty of opportunity in North America.
James Stafford: Related to this, where do you see Canada’s oil and gas industry 10 years from now?
Chris Cooper: I see the oil and gas industry in Canada continuing to grow with the advancement of new drilling techniques and new innovations in exploiting existing pools to increase the recoverability. We have a stable political system in place which enables business opportunity to grow in Canada.
James Stafford: We hear a lot about Alberta, but what kind opportunities are we looking at in Saskatchewan?
Chris Cooper: Saskatchewan is definitely open for business. The royalties paid in Saskatchewan are very low and the production opportunities are very good. We are actively looking for new opportunities in Saskatchewan.

James Stafford: Do you lend any significance to Canadian media reports that the Cabinet is reviewing some new legislation that would set stiff payouts for the oil industry for accidents?
Chris Cooper: Personally, I think it is part of the grand plan to help the approval process. My theory is that the Canadian government will lay out a plan for big fines and then push to have the pipelines approved. The construction of pipeline projects creates jobs and would be good for the economy.
James Stafford: Do you think this is simply a carrot for the protesters at a time when the Enbridge hearings are raising tensions?
Chris Cooper: There will always be protesters.
James Stafford: What do you see happening to energy markets in 2013?
Chris Cooper: I see the price of oil and gas both increasing. They are depleting resources….it’s an inevitable function of supply and demand.
James Stafford: What are your views on the Keystone XL Pipeline? Do you think it is likely that Obama will approve its construction, and if so how will this affect your business?
Chris Cooper: I think he will approve it. The Governor of Nebraska gave the new route the okay. I think now that Obama has been re-elected he will go ahead and approve. Again, that is a lot of job-creation for an economy that is struggling. More pipelines are good for Canadian producers as it helps get our oil to market.
James Stafford: What are Aroway’s top three plays, and why?

Chris Cooper: Our current plays all have different risk and production profiles. We have a large inventory and land spread in our JV lands in the Peace River Arch, which provide a healthy mix of development and exploration risk. Very big upside in this play.
Our West hazel production play is a very stable, long-term production scenario that we feel will provide great cash flow and netbacks to the company. For little investment we feel we can substantially increase production at West Hazel.

Our Kirkpatrick Lake and Little bow lands in central Alberta are also very prospective properties that have the ability to turn into new core areas for the company. All our properties are oil focused and are highly prospective.
James Stafford: What can we expect from Aroway in 2013?
Chris Cooper: I am confident 2013 will be a big growth year for Aroway. We will continue to drill on our existing properties and leverage our production and financial position to take advantage of existing and upcoming opportunities in the sector as far as acquisitions and potential farm-in opportunities. We are confident it will be a big year for our shareholders.
James Stafford: 2013 has been touted as the year of the merger and acquisition. As one of the hottest investments around at the moment, do you think that Canadian junior oil & gas companies, like yourselves, will become a favoured target for larger oil companies looking to expand?
Chris Cooper: I think I can speak for most junior companies when I say, ‘I hope so’. There are a lot of big companies that need to fill the production gaps in their natural depletion, thus in some cases forcing big companies to do that through acquisition. I think the goal for most juniors is to be taken over by a bigger company at a nice premium for the shareholders.
James Stafford: Thanks for your time Chris. For those of you interested in finding out more about Aroway please visit their website at: http://www.arowayenergy.com/