Re-Quantifying Frictions: Long-run Average a Useful Guide for Future?

Economics Group Well Fargo Securities
Quantifying Frictions: Long-run Average a Useful Guide for Future? 11 May 2015
Executive Summary
Keynes said, “This long run is a misleading guide to current affairs. In the long run we are all
dead.” Still, many analysts utilize the “long-run average” (also known as frictionless) concept as a
guideline without regard to possible changing future circumstances. Such projections reflect the
presence of the anchoring bias. In addition, some analysts extend past trends to predict the
future. Is the past trend useful to predict the future? Is there a long-run average that decision
makers can utilize for future guidance? Our answers to both questions: it depends on the
situation.
In other words, we must determine the behavior of a data series before decision makers make a
prediction or utilize a long-run average of the dataset. When we utilize past information, or longrun
averages, the implicit assumption is that the future will be similar to the past. The assumption
that the future would be consistent (similar) with the past has serious consequences, if incorrect,
for econometric analysis and forecasting. The first part of the series explains how standard
econometric models, which assume frictionless (future is similar to the past), may provide
misleading analysis in the presence of frictions.1 The second part of the series presents methods to
quantify frictions in an economy.
Several econometric methods are utilized to quantify frictions in the U.S. economy.2 Some of the
major sectors of the economy, including the labor market, interest rates, financial sector, output,
exchange rates and others, are analyzed. Our econometric analysis found that many series
(employment, productivity, dollar-index, S&P 500, 10-year Treasury and money supply, for
example) are not mean-reverting and experienced structural breaks. Put differently, these
findings imply that if an analyst expects that these series move around a stable average value over
time, then that assumption is incorrect. Furthermore, evidence of structural breaks provides
caution to analysts that future behavior of these series may be different than the past. In addition,
econometric analysis using traditional tools (OLS for example) would provide misleading results
and inaccurate forecasts as well as forecast-bands (confidence intervals). Decision makers should
not put heavy weight on the past average behavior of the series and expect the future will be the
same.

“A Picture Is Worth a Thousand Words:” Frictions or Frictionless?
The first method to identify frictions (a different behavior than the past) in a variable (or a sector)
is the estimation of a long-run trend using the Hodrick-Prescott (H-P) filter
The employment
series’ trend moves upward, most of the time, and since 2000, the series also showed cyclical
behavior. The upward long-run trend of employment, on average, has some noticeable points as
well as a strong indication of frictions (structural breaks). First, the trend experienced several
shifts (breaks) and every break reduced the pace of employment growth (trend becomes flatter).
Second, since early 2000, the trend has flattened and becomes more like a horizontal line, which
indicates a loss of the pre-2000 momentum. Finally, since 2000, the trend shows strong cyclical
behavior and that is different than the pre-2000 behavior, which was an increasing trend. In sum,
the H-P filter based trend shows different behaviors for the various time periods for employment
growth. In other words, a long-run average of employment growth may not be a useful guide for
the future.
The H-P based unemployment rate trend shows strong cyclical patterns The cyclical
behavior of the unemployment rate trend is expected, as unemployment typically tends to move
up during recessions and declines during the expansionary phase of a business cycle. The fed
funds rate trend, on average, has declined over time The long-run trend of productivity
growth  has moved upward over time.4 The rate of growth has accelerated since the
mid-1990s, as trend moved upward (toward left).
In sum, these four graphs show that underlying economic series may have different behavior
during various time periods. That is an indication of frictions, as a frictionless series will show a
consistent behavior over time.

Let’s Put Statistics to Work: Does Volatility Differ Over Time?
We divided employment growth into sub-samples including the pre-Great
Recession (2000-2007) and post-recession (2009-2015) eras. The 1990s experienced high and
stable employment growth, as the highest mean and lowest stability ratio were seen in the 1990s
period. The average growth rates for the 2000-2007 and 2009-2015 periods were similar, but the
post-recession era (2009-2015) showed higher volatility The
2000-2015 period saw the slowest employment growth, on average, and highest stability ratio
(most volatile employment growth). Overall, employment growth showed different behavior in
terms of mean and stability ratio in the various sub-samples.

Two other key indicators of the U.S. labor market, the unemployment rate and average hourly
earnings, also showed signs of frictions The post-Great Recession period observed the
highest unemployment rate along with the lowest wage growth, on average. For all three
measures of the labor market, the 2000-2015 period was most volatile The post-Great Recession era (2009-2014) showed the smallestproductivity growth, on average, and the pre-recession period (1996-2007) saw the highestaverage growth rate. The highest stability ratio was recorded for the 1973-1995 period, whichmarked the most volatile period for productivity growth

Are Markets’ Behaviors Frictionless in the Long-run? Mean-Reversion
Keynes said that the long-run concept is a misleading guide to the current affairs. We can
rephrase the question. Is there a long-run average growth rate, and does the series move around
that average? Basically, we can test whether a variable is mean-reverting (frictionless) in the sense
that the variable moves around its mean growth rate and deviations from that average are
temporary in the long-run. In the next step, we test whether measures of the labor market exhibit
mean-reverting behavior. That is, for example, does employment growth move around an average
value over time and are deviations from the average values temporary?

Are Markets’ Behaviors Frictionless in the Long-run? Mean-Reversion
Keynes said that the long-run concept is a misleading guide to the current affairs. We can
rephrase the question. Is there a long-run average growth rate, and does the series move around
that average? Basically, we can test whether a variable is mean-reverting (frictionless) in the sense
that the variable moves around its mean growth rate and deviations from that average are
temporary in the long-run. In the next step, we test whether measures of the labor market exhibit
mean-reverting behavior. That is, for example, does employment growth move around an average
value over time and are deviations from the average values temporary?

Moving Beyond the Labor Market: Not All Frictions Are Equal
An economy consists of several sectors (markets) and the various markets may perform
differently over time. In addition, individual markets may react to a shock (or to a recession)
differently. Here, we apply the H-P filter on several variables representing several major sectors of
the U.S. economy. The long-run trend along with the log of housing starts, a proxy for the housing
sector  The housing starts trend bottomed out in 2010, well after the official
end date of the Great Recession. Furthermore, the current level of the trend is significantly below
the pre-recession peak, which indicates a slower recovery in the housing sector compared to the
overall economy. The long-run trend of the trade-weighted broad dollar index  ), a proxy
for the foreign exchange market, shows a completely different behavior than the housing starts
series. That is, the dollar’s long-run trend peaked in 2001 and, since then, it showed a declining
trend and bottomed out in 2012. Basically, the dollar trend does not show any significant change
during the Great Recession..

The Great Recession did affect consumer sentiment, as the long-run trend of the consumer
confidence index  dropped to its lowest level since 1985. However, the current level of
the trend is fairly close to the pre-recession peak, which suggests a solid recovery in consumer
sentiment. The recovery in the production side was not the same as the housing sector and
consumer sentiment recoveries. The long-run trend of industrial production, a proxy
for the production sector, is presently at the highest level in our sample period, which starts in
1985. In sum, these variables, representing major sectors of the economy, showed different
behavior and reactions to the Great Recession.6 This finding does shed light on the different types
of frictions issue.

How Volatile Are Some Major Sectors of the U.S. Economy?
. To represent credit/U.S. Treasury markets, the U.S. 10-year Treasury
yield and federal funds target rate are utilized. The lowest 10-year average yields along with the
fed funds rate are for the post Great Recession era (2009-2015). For the 2000-2015 period, the
stability ratio for the fed funds rate was above 100, indicating the most volatile period for the fed
funds rate. The growth rate of the dollar index was very volatile for the complete period as well as
for the sub-samples, as the smallest stability ratio is 442.7. Housing starts and the consumer
confidence index series were also very volatile, as the lowest stability ratios were 182.9 and 136.2,
respectively. S&P 500 returns were very stable during the 1990s, where the stability ratio was
74.2. The pre-Great Recession era (2000-2007) was most volatile for S&P 500 returns, as the
stability ratio was 596.6. The 2000-2015 period reported the highest stability ratios for industrial
production and the ISM manufacturing index. The post Great Recession era observed the highest
average growth rate of the money supply and the highest stability ratio for the PCE deflator was in
the same era. Overall, consistent with the labor market analysis, these major sectors also
experienced different behavior in different sub-samples

Are These Sectors Mean-reverting?
In the next step, we test whether these major sectors were mean-reverting. We found that the
U.S. 10-year Treasury, fed funds and dollar-index were not mean-reverting . The growth
rates of consumer confidence and housing starts were mean-reverting in our sample period
However, these two series were volatile and our econometric analysis found several
outliers in each series. The ISM manufacturing index was also mean-reverting, with possible
volatile behavior The growth rates of money supply and the PCE deflator along with
S&P 500 returns are not mean-reverting
Summing up, we have characterized 14 different variables, representing major sectors of the U.S.
economy, and only three of them (consumer confidence, housing starts and ISM manufacturing
index) turned out to be mean-reverting. The rest of the 11 variables were not mean-reverting. That
indicates the long-run average as a guideline for future can be misleading when projecting future
values of these economic indices.


Concluding Remarks
Several major sectors of the U.S. economy were analyzed to verify Keynes’ notion that the long-run average is a misleading guide for the future. Our econometric analysis found that many series (employment, productivity, dollar-index, S&P 500, 10-year Treasury and money supply, for example) were not mean-reverting and experienced structural breaks. This finding is consistent with Keynes’ idea.
The findings imply that if decision makers expect that these series move around an average value over time, that assumption is incorrect. Furthermore, evidence of structural breaks provides caution that future behavior of these series may be different than in the past. In addition, econometric analysis using traditional tools (OLS for example) would provide misleading analysis and forecasts. In sum, decision makers should not put heavy weight on the past average behavior of these variables as predictors of future values without testing.

My comment-
(1) 2000-庚辰--a chinese bazi  astro combination of big changes
(2) 2009-戊子--a chinese bazi astro combination for new start