Forecasting industry employment for a resource-based economy using bayesian vector autoregressive models

Abstract

Bayesian vector autoregressive (BVAR) models are developed to forecast industry employment for a resource-based economy. Two different types of input-output (I-O) information are used as priors: (i) a reduced-form I-O relationship and (ii) an economic-base version of the I-O information. Out-of-sample forecasts from these two I-O-based BVAR models are compared with forecasts from an autoregressive model, an unconstrained VAR model, and a BVAR model with a Minnesota prior. Results indicate most importantly that overall the model version with economic base information performs the best in the long run. © Southern Regional Science Association 2011.