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

  • Pierre Lafourcade
  • Joris de Wind
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    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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    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. Stephanie Schmitt-Grohe & Martin Uribe, 2001. "Closing Small Open Economy Models," Departmental Working Papers 200115, Rutgers University, Department of Economics.
    2. Giorgio E. Primiceri & Andrea Tambalotti & Alejandro Justiniano, 2009. "Investment Shocks and the Relative Price of Investment," 2009 Meeting Papers 686, Society for Economic Dynamics.
    3. Burriel, Pablo & Fernández-Villaverde, Jesús & Rubio-Ramírez, Juan Francisco, 2009. "MEDEA: A DSGE Model for the Spanish Economy," CEPR Discussion Papers 7297, C.E.P.R. Discussion Papers.
    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. Chang, Yongsung & Doh, Taeyoung & Schorfheide, Frank, 2005. "Non-stationary Hours in a DSGE Model," CEPR Discussion Papers 5232, C.E.P.R. Discussion Papers.
    6. Karl Whelan, 2003. "A two-sector approach to modeling U.S. NIPA data," Open Access publications 10197/203, School of Economics, University College Dublin.
    7. Iskrev, Nikolay, 2010. "Local identification in DSGE models," Journal of Monetary Economics, Elsevier, vol. 57(2), pages 189-202, March.
    8. Smets, Frank & Wouters, Raf, 2007. "Shocks and frictions in US business cycles: a Bayesian DSGE approach," Working Paper Series 0722, European Central Bank.
    9. 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.
    10. 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.
    11. Ferroni, Filippo, 2009. "Trend agnostic one step estimation of DSGE models," MPRA Paper 14550, University Library of Munich, Germany.
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