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      Do DSGE models forecast more accurately out-of sample than VAR models?

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      Author
      Gürkaynak, R.S.
      Kisacikoǧlu, B.
      Rossi, B.
      Date
      2013
      Journal Title
      Advances in Econometrics
      ISSN
      7319053
      Publisher
      JAI Press
      Volume
      32
      Pages
      27 - 79
      Language
      English
      Type
      Article
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      Please cite this item using this persistent URL
      http://hdl.handle.net/11693/21137
      Abstract
      Recently, 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.
      Published as
      http://dx.doi.org/10.1108/S0731-9053(2013)0000031002
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