Money, output and the United States' interwar financial crisis: An empirical analysis

In the first essay of this thesis I test long-run monetary neutrality (LRMN) using the long-horizon approach of Fisher and Seater (18). Using United States' data on M2 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. I 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.