Model Averaging and its Use in Economics

TL;DR

This paper reviews Bayesian and frequentist model averaging methods for addressing model uncertainty in economics, emphasizing covariate selection in linear regression and applications across various economic fields.

Contribution

It provides a comprehensive overview of model averaging techniques, their implementation, and applications specifically tailored to economic research and data analysis.

Findings

Bayesian model averaging highlights the importance of prior assumptions.

Frequentist methods offer alternative approaches to model uncertainty.

Applications span growth economics, finance, and macroeconomic forecasting.

Abstract

The method of model averaging has become an important tool to deal with model uncertainty, for example in situations where a large amount of different theories exist, as are common in economics. Model averaging is a natural and formal response to model uncertainty in a Bayesian framework, and most of the paper deals with Bayesian model averaging. The important role of the prior assumptions in these Bayesian procedures is highlighted. In addition, frequentist model averaging methods are also discussed. Numerical methods to implement these methods are explained, and I point the reader to some freely available computational resources. The main focus is on uncertainty regarding the choice of covariates in normal linear regression models, but the paper also covers other, more challenging, settings, with particular emphasis on sampling models commonly used in economics. Applications of model averaging in economics are reviewed and discussed in a wide range of areas, among which growth economics, production modelling, finance and forecasting macroeconomic quantities.