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The sensitivity of DSGE models’ results to data detrending

Author

Listed:
  • Simona Delle Chiaie

    (Oesterreichische Nationalbank, Economic Studies Division, P.O. Box 61, A-1010 Vienna)

Abstract

This paper aims to shed light on potential pitfalls of different data filtering and detrending procedures for the estimation of stationary DSGE models. For this purpose, a medium-sized New Keynesian model as the one developed by Smets and Wouters (2003) is used to assess the sensitivity of the structural estimates to preliminary data transformations. To examine the question, we focus on two widely used detrending and filtering methods, the HP filter and linear detrending. After comparing the properties of business cycle components, we estimate the model through Bayesian techniques using in turn the two different sets of transformed data. Empirical findings show that posterior distributions of structural parameters are rather sensitive to the choice of detrending. As a consequence, both the magnitude and the persistence of theoretical responses to shocks depend upon preliminary filtering.

Suggested Citation

  • Simona Delle Chiaie, 2009. "The sensitivity of DSGE models’ results to data detrending," Working Papers 157, Oesterreichische Nationalbank (Austrian Central Bank).
    • Handle: RePEc:onb:oenbwp:157
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    Citations

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    Cited by:

    1. Pirmin Fessler & Fabio Rumler & Gerhard Schwarz, 2014. "A micro-based non-inflationary rate of capacity utilisation as a measure of inflationary pressure: evidence for Austria," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 41(1), pages 23-36, February.
    2. Čapek, Jan & Crespo Cuaresma, Jesús & Hauzenberger, Niko & Reichel, Vlastimil, 2023. "Macroeconomic forecasting in the euro area using predictive combinations of DSGE models," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1820-1838.
    3. Baumann, Ursel & Dieppe, Alistair & Dizioli, Allan Gloe, 2017. "Why should the world care? Analysis, mechanisms and spillovers of the destination based border adjusted tax," Working Paper Series 2093, European Central Bank.
    4. Sun Xiaojin & Tsang Kwok Ping, 2019. "What cycles? Data detrending in DSGE models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 23(3), pages 1-23, June.
    5. Stracca, Livio & Bussière, Matthieu, 2010. "A decade (and a global financial crisis) after Blinder: The interaction between researchers and policy-makers in central banks," Working Paper Series 1260, European Central Bank.
    6. Jürgen Jerger & Oke Röhe, 2009. "Testing for Parameter Stability in DSGE Models. The Cases of France, Germany and Spain," Working Papers 276, Leibniz Institut für Ost- und Südosteuropaforschung (Institute for East and Southeast European Studies), revised Mar 2011.
    7. Cristiano Cantore & Paul Levine & Giovanni Melina, 2014. "Deep versus superficial habit: It’s all in the persistence," School of Economics Discussion Papers 0714, School of Economics, University of Surrey.
    8. Efrem Castelnuovo, 2013. "What does a Monetary Policy Shock Do? An International Analysis with Multiple Filters," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(5), pages 759-784, October.
    9. Giorgio Fagiolo & Mauro Napoletano & Marco Piazza & Andrea Roventini, 2009. "Detrending and the Distributional Properties of U.S. Output Time Series," Economics Bulletin, AccessEcon, vol. 29(4), pages 3155-3161.

    More about this item

    Keywords

    DSGE models; Filters; Trends; Bayesian estimates;
    All these keywords.

    JEL classification:

    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles

    NEP fields

    This paper has been announced in the following NEP Reports:

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