Estimating Nonlinear DSGE Models by the Simulated Method of Moments

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.

Strategy-proof preference aggregation

An aggregation rule maps each profile of individual strict preference orderings over a set of alternatives into a social ordering over that set. We call such a rule strategyproof if misreporting one’s preference never produces a social ordering that is strictly between the original ordering and one’s own preference. After describing a few examples of manipulable rules, we study in some detail three classes of strategy-proof rules: (i)rules based on a monotonic alteration of the majority relation generated by the preference profile; (ii)rules improving upon a fixed status-quo; and (iii) rules generalizing the Condorcet-Kemeny aggregation method.