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

Minford, Anthony Patrick Leslie ORCID: https://orcid.org/0000-0003-2499-935X, Xu, Yongdeng ORCID: https://orcid.org/0000-0001-8275-1585 and Zhou, Peng ORCID: https://orcid.org/0000-0002-4310-9474 2014. How good are out of sample forecasting tests on DSGE models? [Working Paper]. Cardiff Economics Working Papers, Cardiff: Cardiff University.

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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.

Item Type:	Monograph (Working Paper)
Date Type:	Publication
Status:	Published
Schools:	Business (Including Economics)
Subjects:	H Social Sciences > HB Economic Theory
Publisher:	Cardiff University
Date of First Compliant Deposit:	30 March 2016
Last Modified:	11 Mar 2023 03:06
URI:	https://orca.cardiff.ac.uk/id/eprint/78013

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