Tilting Approximate Models

TL;DR

This paper introduces a tilting method for approximate models that improves parameter estimation by reducing bias and mean squared error, with applications to US consumption risk pricing.

Contribution

It develops a new tilting estimator that combines approximate models with moment conditions, enhancing econometric accuracy.

Findings

Tilting reduces mean squared error in parameter estimates.

Application to US consumption data improves model fit.

Approximation errors bias risk aversion and intertemporal substitution estimates.

Abstract

Model approximations are common practice when estimating structural or quasi-structural models. The paper considers the econometric properties of estimators that utilize projections to reimpose information about the exact model in the form of conditional moments. The resulting estimator efficiently combines the information provided by the approximate law of motion and the moment conditions. The paper develops the corresponding asymptotic theory and provides simulation evidence that tilting substantially reduces the mean squared error for parameter estimates. It applies the methodology to pricing long-run risks in aggregate consumption in the US, whereas the model is solved using the Campbell and Shiller (1988) approximation. Tilting improves empirical fit and results suggest that approximation error is a source of upward bias in estimates of risk aversion and downward bias in the elasticity of intertemporal substitution.