FORECASTING WITH VECTOR AUTOREGRESSIVE (VAR) MODELS SUBJECT TO BUSINESS-CYCLE RESTRICTIONS

In the last decade VAR models have become a widely-used tool for forec asting macroeconomic time series. To improve the out-of-sample forecas ting accuracy of these models, Bayesian random-walk prior restrictions are often imposed on VAR model parameters. This paper focuses on whet her placing an alternative type of restriction on the parameters of un restricted VAR models improves the out-of-sample forecasting performan ce of these models. The type of restriction analyzed here is based on the business cycle characteristics of U.S. macroeconomic data, and in particular, requires that the dynamic behavior of the restricted VAR m odel mimic the business cycle characteristics of historical data. The question posed in this paper is: would a VAR model, estimated subject to the restriction that the cyclical characteristics of simulated data from the model ''match up'' with the business cycle characteristics o f U.S. data, generate more accurate out-of-sample forecasts than unres tricted or Bayesian VAR models?