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

A. Patrick Minford, Peng Zhou and Yongdeng Xu

No 10239, CEPR Discussion Papers from C.E.P.R. Discussion Papers

Abstract: 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.

Keywords: Dsge; Forecast performance; indirect inference; Out of sample forecasts; Specification tests; Var (search for similar items in EconPapers)
JEL-codes: E10 E17 (search for similar items in EconPapers)
Date: 2014-11
New Economics Papers: this item is included in nep-dge, nep-for and nep-mac
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Related works:
Journal Article: How Good are Out of Sample Forecasting Tests on DSGE Models? (2015)
Working Paper: How good are out of sample forecasting Tests on DSGE models? (2014)
Working Paper: How good are out of sample forecasting Tests on DSGE models? (2014)
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