Comparision of GMM, maximum likelihood and Bayesian estimations in estimating structural parameters of DSGE models

Dynamic stochastic general equilibrium (DSGE) models are macroeconomic models derived from microeconomic principles. These models and estimation methods of their parameters have been very popular among macroeconomists over the past 25 years. Identification of structural parameters of DSGE models is subject of many studies. In this study we compare three estimation methods: Bayesian estimation, maximum likelihood estimation and generalized method of moments estimation, in the cases of (i) if the shocks have an autocorrelated pattern (ii) if data is small. We generate artificial data at the length of 60 and 180 by using our model with true parameters and obtain the estimated parameters from these estimators. Then, for eve-ry estimator, we compare the value of estimated parameters with true ones. The model we use is the three equation New Keynesian model including the Euler condi-tion, Philips curve and monetary policy equations. As a result of comparison, for all cases, with and without autocorrelation and small and large sample sizes, Bayesian estimation performs best. However, it should be noted that if Dynare allowed us to expand the border of priors for Bayesian estima-tor, the result might be different. Keywords: DSGE Models, Bayes. Generalized Method of Moments, Maximum Likelihood, Structural Parameters