Forecast Accuracy of a BVAR under Alternative Specifications of the Zero Lower Bound

This paper discusses how the forecast accuracy of a Bayesian vector autoregression (BVAR) is affected by introducing the zero lower bound on the federal funds rate. As a benchmark I adopt a common BVAR specification, including 18 variables, estimated shrinkage, and no nonlinearity. Then I entertain alternative specifications of the zero lower bound: replace the federal funds rate by its shadow rate, consider a logarithmic transformation, feed in monetary policy shocks, or utilize a rejection sampler. The latter two are also coupled with interest rate expectations from future contracts. The comparison is based on the accuracy of point and density forecasts of major U.S. macroeconomic series during the period 2009:1 to 2014:4. The results show that the performance of the specifications is greatly different, suggesting that this modeling choice is not innocuous. The introduction of the zero lower bound is not beneficial per se, but it depends on how it is done and which series is forecasted. With caution, I recommend the shadow rate specification and the rejection sampler combined with interest rate expectations to deal with the nonlinearity in the policy rate. Since the policy rate will remain low for some time, these findings could prove useful for practical forecasters.