Model Averaging and Its Use in Economics

By Mark F. J. Steel. Journal of Economic Literature, 2017

301 citations recorded by the source corpus.

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 techniques 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 including growth economics, production modeling, finance and forecasting macroeconomic quantities. (JEL C11, C15, C20, C52, O47).

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/5cd3d1e121be005b2f310e677b2789abc5e916b3

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Citations in the corpus

Forecast combinations: An over 50-year review (2022, background): “Two main strands can be identified: frequentist approaches (e.g., Fletcher, 2018) and Bayesian approaches (e.g. Steel, 2020).”
Estimating the Armington elasticity: The importance of study design and publication bias (2020, methodology): “To address this issue, we use both Bayesian (Raftery et al., 1997; Eicher et al., 2011) and frequentist (Hansen, 2007; Amini & Parmeter, 2012) methods of model averaging (Steel, 2020, provides an excellent description of these techniques).”
Bayesian forecasting in economics and finance: A modern review (2022, background): “and Steel, M. F. (2011). Stick-breaking autoregressive processes.”