Bayesian Model Averaging in R

Bayesian model averaging has increasingly witnessed applications across an array of empirical contexts. However, the dearth of available statistical software which allows one to engage in a model averaging exercise is limited. It is common for consumers of these methods to develop their own code, which has obvious appeal. However, canned statistical software can ameliorate one's own analysis if they are not intimately familiar with the nuances of computer coding. Moreover, many researchers would prefer user ready software to mitigate the inevitable time costs that arise when hard coding an econometric estimator. To that end, this paper describes the relative merits and attractiveness of several competing packages in the statistical environment R to implement a Bayesian model averaging exercise.Model Averaging, Zellner's g Prior, BMS

Forecasting using Bayesian and Information Theoretic Model Averaging: An Application to UK Inflation

In recent years there has been increasing interest in forecasting methods that utilise large datasets, driven partly by the recognition that policymaking institutions need to process large quantities of information. Factor analysis is one popular way of doing this. Forecast combination is another, and it is on this that we concentrate. Bayesian model averaging methods have been widely advocated in this area, but a neglected frequentist approach is to use information theoretic based weights. We consider the use of model averaging in forecasting UK inflation with a large dataset from this perspective. We find that an information theoretic model averaging scheme can be a powerful alternative both to the more widely used Bayesian model averaging scheme and to factor models.Forecasting, Inflation, Bayesian model averaging, Akaike criteria, Forecast combining

On the Choice of Prior in Bayesian Model Averaging

Bayesian model averaging attempts to combine parameter estimation and model uncertainty in one coherent framework. The choice of prior is then critical. Within an explicit framework of ignorance we define a ‘suitable’ prior as one which leads to a continuous and suitable analog to the pretest estimator. The normal prior, used in standard Bayesian model averaging, is shown to be unsuitable. The Laplace (or lasso) prior is almost suitable. A suitable prior (the Subbotin prior) is proposed and its properties are investigated.Model averaging;Bayesian analysis;Subbotin prior

A Review of the `BMS' Package for R

This paper describes the relative merits and attractiveness of the newest Bayesian model averaging package, BMS, available in the statistical software R to implement a Bayesian model averaging exercise. This package provides the user with a wide range of customizable priors for conducting a BMA analysis, provides ample graphs to visualize the results and offers several alternative model search mechanisms.Model Averaging, Zellner's g Prior, BMS