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Comparing the DSGE model with the factor model: an out-of-sample forecasting experiment

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  • Wang, Mu-Chun

Abstract

In this paper, we put DSGE forecasts in competition with factor forecasts. We focus on these two models since they represent nicely the two opposing forecasting philosophies. The DSGE model on the one hand has a strong theoretical economic background; the factor model on the other hand is mainly data-driven. We show that by incooperating large information set using factor analysis can indeed improve the short horizon predictive ability, as claimed by manyresearchers. The micro founded DSGE model can provide reasonable forecasts for inflation, especially with growing forecast horizons. To a certain extent, our results are consistent with the prevailling view that simple time series models should be used in short-horizon forecasting and structural models should be used in long-horizon forecasting. Our paper compareds both state-of-the art data-driven and theory-based modelling in a rigorous manner. --

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Bibliographic Info

Paper provided by Deutsche Bundesbank, Research Centre in its series Discussion Paper Series 1: Economic Studies with number 2008,04.

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Date of creation: 2008
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Handle: RePEc:zbw:bubdp1:7115

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Keywords: DSGE models; factor models; forecasting; forecastevaluation;

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Cited by:
  1. Rangan Gupta & Alain Kabundi & Stephen M. Miller, 2009. "Forecasting the US Real House Price Index: Structural and Non-Structural Models with and without Fundamentals," Working papers 2009-42, University of Connecticut, Department of Economics.
  2. Poghosyan, K., 2012. "Structural and reduced-form modeling and forecasting with application to Armenia," Open Access publications from Tilburg University urn:nbn:nl:ui:12-5590845, Tilburg University.
  3. Rangan Gupta & Alain Kabundi, 2009. "A Large Factor Model for Forecasting Macroeconomic Variables in South Africa," Working Papers 137, Economic Research Southern Africa.
  4. Wieland, Volker & Wolters, Maik H, 2010. "The Diversity of Forecasts from Macroeconomic Models of the U.S. Economy," CEPR Discussion Papers 7870, C.E.P.R. Discussion Papers.
  5. Wolters, Maik H., 2011. "Forecasting under Model Uncertainty," Annual Conference 2011 (Frankfurt, Main): The Order of the World Economy - Lessons from the Crisis 48723, Verein für Socialpolitik / German Economic Association.
  6. Stelios Bekiros & Alessia Paccagnini, 2013. "On the predictability of time-varying VAR and DSGE models," Empirical Economics, Springer, vol. 45(1), pages 635-664, August.
  7. Falch, Nina Skrove & Nymoen, Ragnar, 2011. "The accuracy of a forecast targeting central bank," Economics Discussion Papers 2011-6, Kiel Institute for the World Economy.
  8. Bušs, Ginters, 2009. "Comparing forecasts of Latvia's GDP using simple seasonal ARIMA models and direct versus indirect approach," MPRA Paper 16684, University Library of Munich, Germany.
  9. Wolters, Maik Hendrik, 2012. "Evaluating point and density forecasts of DSGE models," IMFS Working Paper Series 59, Institute for Monetary and Financial Stability (IMFS), Goethe University Frankfurt.
  10. Christoffel, Kai & Warne, Anders & Coenen, Günter, 2010. "Forecasting with DSGE models," Working Paper Series 1185, European Central Bank.

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