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 2015. How good are out of sample forecasting tests on DSGE models? Italian Economic Journal 1 (3) , pp. 333-351. 10.1007/s40797-015-0020-9

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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 and (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:	Article
Date Type:	Publication
Status:	Published
Schools:	Business (Including Economics)
Subjects:	H Social Sciences > H Social Sciences (General)
Publisher:	Springer
ISSN:	2199-322X
Date of First Compliant Deposit:	30 March 2016
Date of Acceptance:	9 July 2015
Last Modified:	16 Nov 2024 13:15
URI:	https://orca.cardiff.ac.uk/id/eprint/75348

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