Developing a Bayesian vector autoregression forecasting model

Abstract In recent years, Bayesian vector autoregression (BVAR) forecasting models have demonstrated considerable success in forecasting macroeconomic and regional economic variables. In spite of this success, these promising forecasting models have yet to be widely used in business forecasting. This is due, in part, to the rather formidable practical problem of specifying an appropriate BVAR forecasting model. The purpose of this paper is to simplify the model selection process by offering a systematic BVAR forecasting model selection procedure that is readily implemented using a popular software package. A practical five-step procedure is presented and then illustrated using a business forecasting application.

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