Discussion paper

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

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.

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Citation

Minford, P (2014), ‘DP10239 How good are out of sample forecasting Tests on DSGE models?‘, CEPR Discussion Paper No. 10239. CEPR Press, Paris & London. https://cepr.org/publications/dp10239