The Phillips Curve as an Artifact of Austrian Business Cycle Theory

0:00When we estimate a Phillips curve, the relationship between inflation and unemployment, we expect to find a negative coefficient on inflation, but instead we often find that this coefficient is significant and positive. Furthermore, if we estimate a simple OLS autoregressive distributed lag specification of the kind proposed by William Niskanen in his 2002 Cato Journal article, we find that the coefficient on current Inflation is negative, consistent with the original Phillips curve and its Keynesian interpretation, but coefficients on lagged inflation are positive and significantly larger. This result can only be explained by Austrian business cycle theory. Inflation lowers unemployment in the short run while raising it and by a greater amount in the long run. The lowered unemployment initially brought about by inflation, the traditional Phillips curve, is the unsustainable Model Expansion Phase of the Austrian Business Cycle.

0:59The higher unemployment which follows is the process of malinvestment liquidation which cannot be explained by any other model of the business cycle. Niskanen's results were criticized to the effect that he should have estimated vector error correction models. We start by examining the consumer price index in a simple OLS regression where it is explained by Various Monetary Aggregates. These have very high R squares. Because all the data here are non-stationary, the estimates are super consistent, though residual tests suggest the monetary data should be put in logarithms. Recent empirical findings that monetary aggregates don't actually contribute to forecasting future consumer price index, combined with the fact that they do contribute here to explaining the current CPI, suggest that prices merely rise contemporaneously with the money supply.

1:55These residuals suggest structural changes with a marked increase in short-term volatility after 1995. In general, to overcome this problem, regressions and error correction models were estimated over truncated samples. Estimating a Phillips curve vector error correction model for 1948-2010, in the cointegrating In the following equation, the coefficient on inflation is positive and significant, meaning that higher inflation means higher unemployment, and the intercept suggests a natural rate of unemployment of about 4%. This result is robust to changing the lag specification in the disequilibrium adjustment process, truncating the sample range and adding the three-month Treasury bill interest rate.

2:42It could be questioned whether an error correction model is appropriate, however, because all All the data are stationary, that is, they don't have a long-term trend. The disequilibrium adjustment process, assuming 1 to 24 months of lag changes in unemployment and inflation, shows that every 1% in inflation raises unemployment by just under 1% after for years and this permanent increase in unemployment starts after about 14 months. Truncating the disequilibrium adjustment process to 6 to 18 months results in a slightly smaller total increase in unemployment with an earlier onset, but this seems to be an artifact of removing part of the actual process of adjustment.

3:34Adding the short-term interest rate gives the same result. Note that over the first two years, higher interest rates, looking now at the lower of these two graphs, lower unemployment slightly. This seems to be because the monetary expansion which results in inflation is implemented through targeting lower interest rates. This outcome is also robust to truncating the disequilibrium adjustment process. Now those were all over 1948 to 2010, over the period of 2003 to 2010, every 1.57% increase in inflation has resulted in a 4% increase in unemployment, an even less favorable trade-off.

4:20The period of onset of increased unemployment is also shortened. This is hardly changed by truncating the disequilibrium adjustment process. Next we examine a vector error correction model of two non-stationary processes, which are the kind of data that vector error correction models are really intended for. And these are civilian unemployment and the consumer price index. The two underlying data sets are variables that the unemployment rate and the inflation rate depend on. The cointegrating equation here gives us a positive and significant coefficient on the consumer price index, suggesting that a rising consumer price index accompanies higher employment in equilibrium.

5:11Now for the first error correction model, lower unemployment was good. Over 1948 to 2010, the disequilibrium adjustment process indicates a loss of 160,000 jobs for for every one point increase in the consumer price index. And here, as opposed to the earlier vector error correction model, the fact that it goes down indicates that when you raise the consumer price index, you lose jobs.

6:06Residual plots suggest structural changes in the CPI process, which is the one on the in the bottom, but not in the employment process, which is probably the more important one, which is the one on the top, because that's what we care about explaining. Over the 1982 to 2010 period, every one point increase in the CPI costs over 200,000 jobs, and it gets worse. Over the 1990 to 2010 period, every one point increase in the consumer price index costs There's over 480,000 jobs, almost half a million, but things aren't all. There is a silver lining. Over the 2003 to 2010 period, the most recent experience, every one point increase in the consumer price index costs a mere 409,000 jobs.

6:58Is this just an artifact of a combination of fixed weight CPI recently understating price increases and some actual price deflation which occurred during the recession and was is fairly unique to this period in the data set, or is the Keynesian resurgence actually improving the economy? No, it's not.

7:22Employment and MZM, money of zero maturity, are non-stationary variables for which vector error correction model is also appropriate. Increases in MZM lower employment in the cointegrating equation. This is the opposite of what was found with the consumer price Index. The coefficient is negative and significant and is robust to shortening the sample range. In the disequilibrium adjustment process for 1959 to 2010, every $1 billion increase in MZM wiped out 2,500 jobs after four years. Residual plots suggest structural instability in the MZM equation, but not in the employment equation. And generally, the interpretation One would be that MZM is more exogenous than employment, which depends on it.

8:15Since 1982, now this is with a truncated sample, every $1 billion increase in MZM has destroyed 4,125 jobs. Since 1990, every $1 billion increase in MZM has destroyed nearly 10,000 jobs, and things These do get a little better. Since 2003, every $1 billion increase in MZM has only cost us 6,350 jobs. Another attractive vector error correction model to examine is in employment, the monetary base and the Austrian money supply. The monetary base is not statistically significant in the cointegrating equation, but the Austrian And money supply is always negative and significant.

9:04Over the 2003 to 2010 period, in the disequilibrium adjustment process, every $1 billion increase in the Austrian money supply wipes out approximately 5,000 jobs. And that's very consistent with what we found with MZM. These residuals don't suggest structural breaks, but there's less volatility in the 2009 to 2010 period. This relationship between employment, unemployment, output, income, and general economic activity, on the one hand, and the money supply prices and inflation, on the other hand, is dramatically different from what has traditionally been supposed.

9:56The conventional view of the Phillips curve that there is a trade-off between inflation and unemployment is true at times, but is strictly a short-run phenomenon. Sometimes this relationship is reversed even in the short-run. It seems to be always a negative relationship in the long-run, the long-run meaning six to 48 months or longer, and a six-month long-run is not very long. That inflation decreases unemployment in the short run is consistent with the unsustainable expansion phase of Austrian business cycle theory. The much stronger and more robust finding that inflation increases unemployment and by much larger amounts over the longer run can only be explained by Austrian business cycle theory. This is an important result which strongly disconfirms all other models of the business cycle and this result is highly robust from a from a statistical perspective.