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The Incremental Predictive Information Associated with Using Theoretical New Keynesian DSGE Models vs. Simple Linear Econometric Models

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  • Oleg Korenok
  • Norman R. Swanson

Abstract

In this paper we construct output gap and inflation predictions using a variety of dynamic stochastic general equilibrium (DSGE) sticky price models. Predictive density accuracy tests related to the test discussed in Corradi and Swanson ["Journal of Econometrics" (2005a), forthcoming] as well as predictive accuracy tests due to Diebold and Mariano ["Journal of Business and Economic Statistics" (1995), Vol. 13, pp. 253-263]; and West ["Econometrica" (1996), Vol. 64, pp. 1067-1084] are used to compare the alternative models. A number of simple time-series prediction models (such as autoregressive and vector autoregressive (VAR) models) are additionally used as strawman models. Given that DSGE model restrictions are routinely nested within VAR models, the addition of our strawman models allows us to indirectly assess the usefulness of imposing theoretical restrictions implied by DSGE models on unrestricted econometric models. With respect to predictive density evaluation, our results suggest that the standard sticky price model discussed in Calvo ["Journal of Monetary Economics" (1983), Vol. XII, pp. 383-398] is not outperformed by the same model augmented either with information or indexation, when used to predict the output gap. On the other hand, there are clear gains to using the more recent models when predicting inflation. Results based on mean square forecast error analysis are less clear-cut, although the standard sticky price model fares best at our longest forecast horizon of 3 years, it performs relatively poorly at shorter horizons. When the strawman time-series models are added to the picture, we find that the DSGE models still fare very well, often outperforming our forecast competitions, suggesting that theoretical macroeconomic restrictions yield useful additional information for forming macroeconomic forecasts. Copyright 2005 Blackwell Publishing Ltd.

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Bibliographic Info

Article provided by Department of Economics, University of Oxford in its journal Oxford Bulletin of Economics & Statistics.

Volume (Year): 67 (2005)
Issue (Month): s1 (December)
Pages: 905-930

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Handle: RePEc:bla:obuest:v:67:y:2005:i:s1:p:905-930

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Cited by:
  1. Norman Swanson & Oleg Korenok & Stanislav Radchenko, 2006. "International Evidence on the Efficacy of new-Keynesian Models of Inflation Persistence," Departmental Working Papers 200617, Rutgers University, Department of Economics.
  2. Ali Dib & Mohamed Gammoudi & Kevin Moran, 2005. "Forecasting Canadian Time Series with the New-Keynesian Model," Cahiers de recherche 0527, CIRPEE.
  3. N. Gregory Mankiw & Ricardo Reis, 2006. "Sticky Information in General Equilibrium," NBER Working Papers 12605, National Bureau of Economic Research, Inc.
  4. Ghent, Andra, 2006. "Comparing Models of Macroeconomic Fluctuations: How Big Are the Differences?," MPRA Paper 180, University Library of Munich, Germany.
  5. N. Gregory Mankiw & Ricardo Reis, 2001. "Sticky Information Versus Sticky Prices: A Proposal to Replace the New Keynesian Phillips Curve," Harvard Institute of Economic Research Working Papers 1922, Harvard - Institute of Economic Research.
  6. Ricardo Reis, 2008. "A Sticky-Information General Equilibrium Model for Policy Analysis," Working Papers Central Bank of Chile 495, Central Bank of Chile.
  7. Oleg Korenok, 2005. "Empirical Comparison of Sticky Price and Sticky Information Models," Macroeconomics 0510004, EconWPA.
  8. Ricardo Reis, 2009. "A Sticky-Information General-Equilibrium Model for Policy Analysis," NBER Working Papers 14732, National Bureau of Economic Research, Inc.
  9. Rubaszek, Michal & Skrzypczynski, Pawel, 2008. "On the forecasting performance of a small-scale DSGE model," International Journal of Forecasting, Elsevier, vol. 24(3), pages 498-512.
  10. Ghent, Andra C., 2009. "Comparing DSGE-VAR forecasting models: How big are the differences?," Journal of Economic Dynamics and Control, Elsevier, vol. 33(4), pages 864-882, April.

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