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Taking a DSGE Model to the Data Meaningfully

  • Franchi, Massimo
  • Jusélius, Katarina

All economists say that they want to take their model to the data. But with incomplete and highly imperfect data, doing so is difficult and requires carefully matching the assumptions of the model with the statistical properties of the data. The cointegrated VAR (CVAR) offers a way of doing so. In this paper we outline a method for translating the assumptions underlying a DSGE model into a set of testable assumptions on a cointegrated VAR model and illustrate the ideas with the RBC model in Ireland (2004). Accounting for unit roots (near unit roots) in the model is shown to provide a powerful robustification of the statistical and economic inference about persistent and less persistent movements in the data. We propose that all basic assumptions underlying the theory model should be formulated as a set of testable hypotheses on the long-run structure of a CVAR model, a so called ?theory consistent hypothetical scenario?. The advantage of such a scenario is that if forces us to formulate all testable implications of the basic hypotheses underlying a theory model. We demonstrate that most assumptions underlying the DSGE model and, hence, the RBC model are rejected when properly tested. Leaving the RBC model aside, we then report a structured CVAR analysis that summarizes the main features of the data in terms of long-run relations and common stochastic trends. We argue that structuring the data in this way offers a number of ?sophisticated? stylized facts that a theory model has to replicate in order to claim empirical relevance.

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Paper provided by Kiel Institute for the World Economy in its series Economics Discussion Papers with number 2007-6.

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Date of creation: 2007
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Handle: RePEc:zbw:ifwedp:5520
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  1. Peter N. Ireland, 1999. "A method for taking models to the data," Working Paper 9903, Federal Reserve Bank of Cleveland.
  2. George J. Hall, 1994. "Overtime, effort and the propagation of business cycle shocks," Working Paper Series, Macroeconomic Issues 94-25, Federal Reserve Bank of Chicago.
  3. Altug, Sumru, 1989. "Time-to-Build and Aggregate Fluctuations: Some New Evidence," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 30(4), pages 889-920, November.
  4. Heino Bohn Nielsen, 2004. "Cointegration analysis in the presence of outliers," Econometrics Journal, Royal Economic Society, vol. 7(1), pages 249-271, 06.
  5. Bencivenga, Valerie R, 1992. "An Econometric Study of Hours and Output Variation with Preference Shocks," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 33(2), pages 449-71, May.
  6. Ingram, B.F. & Kocherlakota, N.R. & Savin, N.E., 1992. "Explaining Business Cycles : A Multiple Shock Approach," Working Papers 92-09, University of Iowa, Department of Economics.
  7. McGrattan, Ellen R., 1994. "The macroeconomic effects of distortionary taxation," Journal of Monetary Economics, Elsevier, vol. 33(3), pages 573-601, June.
  8. Gary Hansen, 2010. "Indivisible Labor and the Business Cycle," Levine's Working Paper Archive 233, David K. Levine.
  9. Kim, Jinill, 2000. "Constructing and estimating a realistic optimizing model of monetary policy," Journal of Monetary Economics, Elsevier, vol. 45(2), pages 329-359, April.
  10. Juselius, Katarina, 2006. "The Cointegrated VAR Model: Methodology and Applications," OUP Catalogue, Oxford University Press, number 9780199285679.
  11. Davidson, James, 1998. "Structural relations, cointegration and identification: some simple results and their application," Journal of Econometrics, Elsevier, vol. 87(1), pages 87-113, August.
  12. DeJong, David N. & Ingram, Beth F. & Whiteman, Charles H., 2000. "A Bayesian approach to dynamic macroeconomics," Journal of Econometrics, Elsevier, vol. 98(2), pages 203-223, October.
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