Computational Experiments and Reality

A formal probability framework that unifies general equilibrium models (theory without measurement) and atheoretical econometrics (measurement without theory) is provided and some models of the equity premium are illustrated.

Abstract

This study explores three alternative econometric interpretations of dynamic, stochastic general equilibrium (DSGE) models. (1) A strong econometric interpretation takes the model literally and directly produces a likelihood function for observed prices and quantities. It is widely recognized that under this interpretation, most DSGE models are rejected using classical econometrics and assigned zero probability in a Bayesian approach. (2) A weak econometric interpretation commonly made in the calibration literature confines attention to only a few functions of observed prices and interest rates and evaluates a model on its predictive distribution for these functions. This approach is equivalent to a Bayesian prior predictive analysis, developed by Box (1980) and predecessors. This study shows that the weak interpretation retains the implications of the strong interpretation, and therefore DSGE’s fare no better under this approach. (3) Under a minimal econometric interpretation, DSGE’s provide only prior distributions for specified population moments. When coupled with an econometric model (e.g., a vector autoregression) that includes the same moments, DSGE’s may be compared and used for inference using conventional Bayesian methods. This interpretation extends and formalizes an approach suggested by Dejong, Ingram and Whiteman (1996). All three interpretations are illustrated using models of the equity premium, and it is shown that the conclusions from a minimal interpretation differ substantially from those under a weak interpretation. This revision was prepared for the DYNARE Conference, CEPREMAP, Paris, September 4-5, 2006. It is work in progress. Comments welcome. Please do not cite or quote without the author’s permission. 1 1