Estimating Nonlinear Dynamic Equilibrium Economies: A Likelihood Approach

Abstract

This paper presents a framework to undertake likelihood-based inference in nonlinear dynamic equilibrium economies. We develop a Sequential Monte Carlo algorithm that delivers an estimate of the likelihood function of the model using simulation methods. This likelihood can be used for parameter estimation and for model comparison. The algorithm can deal both with nonlinearities of the economy and with the presence of non-normal shocks. We show consistency of the estimate and its good performance in finite simulations. This new algorithm is important because the existing empirical literature that wanted to follow a likelihood approach was limited to the estimation of linear models with Gaussian innovations. We apply our procedure to estimate the structural parameters of the neoclassical growth model.

Keywords: Likelihood-Based Inference, Dynamic Equilibrium Economies, Nonlinear Filtering, Sequential Monte Carlo

JEL Classification: C10, C11, C13, C15