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Taking Trends Seriously in DSGE Models: An Application to the Dutch Economy

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  • Pierre Lafourcade
  • Joris de Wind
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    Abstract

    We construct a new-Keynesian DSGE model tailored to the Netherlands and interpret it as a multivariate unobserved components model. We identify three major stochastic trends in the data—trends in general-purpose technology, investment-specific technology, and labor supply—and model them formally in our theoretical set-up. Our trend-cycle decomposition captures the data's co-integrating properties without which long-run analysis—whether scenario analysis or forecasting—would likely be misspecified. In particular, this approach appears to produce better-behaved posteriors for parameters along decision margins where traditional modeling imposes highly persistent but temporary shocks. The existence of permanent and temporary disturbances along the same margin broadens the scope for counterfactuals. Specifically, differences in short-run responses to the two types of shocks reflect smoothing motives and discounted valuation effects reminiscent of the Permanent Income Hypothesis.

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    Bibliographic Info

    Paper provided by Netherlands Central Bank, Research Department in its series DNB Working Papers with number 345.

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    Date of creation: Jul 2012
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    Handle: RePEc:dnb:dnbwpp:345

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    1. Ferroni, Filippo, 2009. "Trend agnostic one step estimation of DSGE models," MPRA Paper 14550, University Library of Munich, Germany.
    2. Stephanie Schmitt-Grohe & Martin Uribe, 2001. "Closing Small Open Economy Models," Departmental Working Papers 200115, Rutgers University, Department of Economics.
    3. Pablo Burriel & Jesús Fernández-Villaverde & Juan F. Rubio-Ramírez, 2009. "MEDEA: A DSGE Model for the Spanish Economy," Working Papers 2009-17, FEDEA.
    4. Adolfson, Malin & Laséen, Stefan & Lindé, Jesper & Villani, Mattias, 2007. "Evaluating An Estimated New Keynesian Small Open Economy Model," Working Paper Series 203, Sveriges Riksbank (Central Bank of Sweden).
    5. Yongsung Chang & Taeyoung Doh & Frank Schorfheide, 2007. "Non-stationary Hours in a DSGE Model," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(6), pages 1357-1373, 09.
    6. Whelan, Karl, 2003. " A Two-Sector Approach to Modeling U.S. NIPA Data," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 35(4), pages 627-56, August.
    7. Nikolay Iskrev, 2009. "Local Identification in DSGE Models," Working Papers w200907, Banco de Portugal, Economics and Research Department.
    8. Justiniano, Alejandro & Primiceri, Giorgio E. & Tambalotti, Andrea, 2009. "Investment Shocks and the Relative Price of Investment," CEPR Discussion Papers 7598, C.E.P.R. Discussion Papers.
    9. Frank Smets & Rafael Wouters, 2007. "Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach," American Economic Review, American Economic Association, vol. 97(3), pages 586-606, June.
    10. Cristiano Cantore & Miguel León-Ledesma & Peter McAdam & Alpo Willman, 2014. "Shocking Stuff: Technology, Hours, And Factor Substitution," Journal of the European Economic Association, European Economic Association, vol. 12(1), pages 108-128, 02.
    11. Kevin D. Hoover & Katarina Juselius & Søren Johansen, 2007. "Allowing the Data to Speak Freely: The Macroeconometrics of the Cointegrated Vector Autoregression," Discussion Papers 07-35, University of Copenhagen. Department of Economics.
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    Cited by:
    1. Tony Hall & Jan Jacobs & Adrian Pagan, 2013. "Macro-Econometric System Modelling @75," CAMA Working Papers 2013-67, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    2. Kai Liu, 2014. "Public Finances, Business Cycles and Structural Fiscal Balances," Cambridge Working Papers in Economics 1411, Faculty of Economics, University of Cambridge.

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