How Good are Out of Sample Forecasting Tests on DSGE Models?
A. Patrick Minford,
Yongdeng Xu and
Peng Zhou
Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, 2015, vol. 1, issue 3, 333-351
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. Copyright Società Italiana degli Economisti (Italian Economic Association) 2015
Keywords: Out of sample forecasts; DSGE; VAR; Specification tests; Indirect inference; Forecast performance; E10; E17 (search for similar items in EconPapers)
Date: 2015
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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)
Working Paper: How good are out of sample forecasting Tests on DSGE models? (2014)
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DOI: 10.1007/s40797-015-0020-9
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