| Summary: | In the first essay of this thesis I test long-run monetary neutrality (LRMN) using the longhorizon
approach of Fisher and Seater [18]. Using United States' data on M 2 and Net
National Product they reject LRMN for the sample 1869-1975. However, I show that this
result is not robust to the use of the monetary base instead of M2. Nor is it robust to the use
of United Kingdom data instead of United States data. These results are consistent with the
interpretation that Fisher and Seater's result is a consequence of the financial crisis of the
1930s causing inside money and output to move together. Using a Monte Carlo study I show
that Fisher and Seater's rejection of LRMN can also be accounted for by size distortion in
their test statistic. This study also shows that at longer horizons, power is very low.
In the second essay I consider the financial crisis of the 1930s in the United States as
change in regime. Using a bivariate version of Hamilton's [24] Markov switching model I
estimate the probability that the underlying regime was one of financial crisis at each point in
time. I argue that there was a shift to the financial crisis regime following the first banking
crisis of 1930. The crucial reform in ending the financial crisis appears to have been the
introduction of the Federal Deposit Insurance Corporation in January 1934.1 also find that
the time series of probabilities over the state of the financial sector contain marginal
explanatory power for output fluctuations in the inter-war period.
A problem when testing the null hypothesis of a linear model against the alternative
of the Markov switching model is the presence of nuisance parameters. Consequently, the
likelihood ratio test statistic does not possess the standard chi-squared distribution. In my
third essay I perform a Monte Carlo experiment to explore the small sample properties of the pseudo likelihood ratio test statistic under the non-standard conditions. I find no evidence of
size distortion. However, I do find that size adjusted power is very poor in small samples.
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