book

Oral Communications
CO5
Thursday, September 5th
18:00
DSGE Estimation using Generalized Empirical
Likelihood and Generalized Minimum Contrast
Gilberto Oliveira Boaretto
PUC/Rio
Márcio Laurini
The objective of this work is to investigate the performance of moment-based estimators of
the generalized empirical likelihood (GEL) and generalized minimum contrast (GMC) families in
estimation of dynamic stochastic general equilibrium (DSGE) models, with special attention to
the robustness properties under misspecification. As benchmark we used generalized method of
moments (GMM), maximum likelihood (ML) and Bayesian inference (BI).We work with a real
business cycle (RBC) model considered the core of DSGE models and that has a smaller number
of parameters, which facilitates a analysis of the estimators. From the Monte Carlo experiments
we found that (i) empirical likelihood (EL) estimator - as well as its version with smoothed moment
conditions (SEL) - and Bayesian inference (BI) obtained, in that order, the best performances,
including misspecification cases; (ii) continuous updating empirical likelihood (CUE), minimum
Hellinger distance (HD), exponential tilting (ET) estimators and their smoothed versions exhibit
intermediate comparative performance; (iii) performance of exponentially tilted empirical likelihood
(ETEL), exponential tilting Hellinger distance (ETHD) and its smoothed versions was seriously
compromised by atypical estimates; (iv) smoothed and non-smoothed GEL/GMC estimators exhibit
very similar performances; (v) GMM, especially in over-identified case, and ML estimators performed
worse than their competitors.
Keywords: Dynamic stochastic general equilibrium models; Method of moments; Empirical
likelihood; Minimum contrast; Robustness
42