DP10239 How good are out of sample forecasting Tests on DSGE models?

Author(s): Patrick Minford, Yongdeng Xu, Peng Zhou
Publication Date: November 2014
Keyword(s): DSGE, forecast performance, indirect inference, out of sample forecasts, specification tests, VAR
JEL(s): E10, E17
Programme Areas: International Macroeconomics
Link to this Page: cepr.org/active/publications/discussion_papers/dp.php?dpno=10239

Out-of-sample forecasting tests of DSGE models against time-series benchmarks such as an unrestricted VAR are increasingly used to check a) the specification b) the forecasting capacity of these models. We carry out a Monte Carlo experiment on a widely-used DSGE model to investigate the power of these tests. We find that in specification testing they have weak power relative to an in-sample indirect inference test; this implies that a DSGE model may be badly mis-specified and still improve forecasts from an unrestricted VAR. In testing forecasting capacity they also have quite weak power, particularly on the lefthand tail. By contrast a model that passes an indirect inference test of specification will almost definitely also improve on VAR forecasts.