A BVAR model for the connecticut economy

Abstract

Abstract A Bayesian vector autoregressive (BVAR) model is developed for the Connecticut economy to forecast the unemployment rate, nonagricultural employment, real personal income, and housing permits authorized. The model includes both national and state variables. The Bayesian prior is selected on the basis of the accuracy of the out‐of‐sample forecasts. We find that a loose prior generally produces more accurate forecasts. The out‐of‐sample accuracy of the BVAR forecasts is also compared with that of forecasts from an unrestricted VAR model and of benchmark forecasts generated from univariate ARIMA models. The BVAR model generally produces the most accurate short‐ and long‐term out‐of‐sample forecasts for 1988 through 1992. It also correctly predicts the direction of change.