My dissertation uses Monte Carlo simulations to evaluate alternative
identification strategies in VAR estimation of monetary models, and to
assess the accuracy of measuring money instability as a cause of output
fluctuations. I construct theoretical monetary economies using general
equilibrium models with cash-in-advance constraints, which also include
technology shocks, labor supply shocks, and monetary shocks.
Particularly, two economies are characterized: one is fully identified
and satisfies the long-run restriction; another is not fully identified
and the portion of temporary technology shocks is mixed with demand
shocks when applying the long-run restriction. Based on each theoretical
model, artificial economies are then generated through Monte Carlo
simulations, which allow me to investigate the reliability of structural
VAR estimation under various identifying restrictions.
Applying
short-run, medium-run, and long-run restrictions on the simulated data, I
check for the bias between the average VAR estimates and the true
theoretical claim. The findings show that short-run and medium-run
restrictions tend to work better under model uncertainty, particularly
because the bias for measuring the effects of monetary shocks using
long-run restriction could increase substantially when the underlying
economy includes unidentified temporary shocks. This experiment supports
the claim that monetary shocks contribute no more than one third of the
cyclical variance of post-war U.S. output, and suggests that their
contribution could in fact be substantially less.
