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Estimating Nonlinear DSGE Models by the Simulated Method of Moments

  • Francisco J. Ruge-Murcia

    ()

    (Department of Economics, University of Montréal; The Rimini Centre for Economic Analysis (RCEA))

This paper studies the application of the simulated method of moments (SMM) for the estimation of nonlinear dynamic stochastic general equilibrium (DSGE) models. Monte Carlo analysis is employed to examine the small-sample properties of SMM in specifications with different curvature. Results show that SMM is computationally efficient and delivers accurate estimates, even when the simulated series are relatively short. However, asymptotic standard errors tend to overstate the actual variability of the estimates and, consequently, statistical inference is conservative. A simple strategy to incorporate priors in a method of moments context is proposed. An empirical application to the macroeconomic effects of rare events indicates that negatively skewed productivity shocks induce agents to accumulate additional capital and can endogenously generate asymmetric business cycles.

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Paper provided by The Rimini Centre for Economic Analysis in its series Working Paper Series with number 49_10.

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Date of creation: Jan 2010
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Handle: RePEc:rim:rimwps:49_10
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