How good are out of sample forecasting Tests on DSGE models?
A. Patrick Minford,
Yongdeng Xu () and
Peng Zhou ()
No 10239, CEPR Discussion Papers from C.E.P.R. Discussion Papers
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)
New Economics Papers: this item is included in nep-dge, nep-for and nep-mac
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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)
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