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dc.contributor.authorGürkaynak, Refet S.en_US
dc.contributor.authorKisacikoǧlu, B.en_US
dc.contributor.authorRossi, B.en_US
dc.date.accessioned2016-02-08T09:41:43Z
dc.date.available2016-02-08T09:41:43Z
dc.date.issued2013en_US
dc.identifier.issn0731-9053
dc.identifier.urihttp://hdl.handle.net/11693/21137
dc.description.abstractRecently, it has been suggested that macroeconomic forecasts from estimated dynamic stochastic general equilibrium (DSGE) models tend to be more accurate out-of-sample than random walk forecasts or Bayesian vector autoregression (VAR) forecasts. Del Negro and Schorfheide (2013) in particular suggest that the DSGE model forecast should become the benchmark for forecasting horse-races. We compare the real-time forecasting accuracy of the Smets and Wouters (2007) DSGE model with that of several reduced-form time series models. We first demonstrate that none of the forecasting models is efficient. Our second finding is that there is no single best forecasting method. For example, typically simple AR models are most accurate at short horizons and DSGE models are most accurate at long horizons when forecasting output growth, while for inflation forecasts the results are reversed. Moreover, the relative accuracy of all models tends to evolve over time. Third, we show that there is no support to the common practice of using large-scale Bayesian VAR models as the forecast benchmark when evaluating DSGE models. Indeed, low-dimensional unrestricted AR and VAR forecasts may forecast more accurately. Copyright © 2013 by Emerald Group Publishing Limited.en_US
dc.language.isoEnglishen_US
dc.source.titleAdvances in Econometricsen_US
dc.relation.isversionofhttp://dx.doi.org/10.1108/S0731-9053(2013)0000031002en_US
dc.subjectBayesian VARen_US
dc.subjectDSGEen_US
dc.subjectForecast optimalityen_US
dc.subjectForecastingen_US
dc.subjectReal-time dataen_US
dc.titleDo DSGE models forecast more accurately out-of sample than VAR models?en_US
dc.typeArticleen_US
dc.departmentDepartment of Economicsen_US
dc.citation.spage27en_US
dc.citation.epage79en_US
dc.citation.volumeNumber32en_US
dc.identifier.doi10.1108/S0731-9053(2013)0000031002en_US
dc.publisherEmeralden_US


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