# Model Averaging and its Use in Economics

arXiv: https://arxiv.org/abs/1709.08221 · HTML: https://mlsanity.com/paper/1709.08221/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2017.

## Built on by (most cited first, 5 of 5)

- [Forecast combinations: an over 50-year review](https://mlsanity.com/paper/2205.04216/) (2022)
- [Variational Inference for Bayesian Neural Networks under Model and Parameter Uncertainty](https://mlsanity.com/paper/2305.00934/) (2023)
- [Model Averaging and Double Machine Learning](https://mlsanity.com/paper/2401.01645/) (2024)
- [Ensemble Prediction via Covariate-dependent Stacking](https://mlsanity.com/paper/2408.09755/) (2024)
- [Still no evidence for an effect of the proportion of non-native speakers on language complexity -- A response to Kauhanen, Einhaus & Walkden (2023)](https://mlsanity.com/paper/2305.00217/) (2023)

## Abstract (arXiv)

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.