Abstract:

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.