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Using HP Filtered Data for Econometric Analysis: Some Evidence from Monte Carlo Simulations

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  • Mark Meyer
  • Peter Winker*

Abstract

The Hodrick-Prescott (HP) filter has become a widely used tool for detrending integrated time series in applied econometric analysis. Even though the theoretical time series literature sums up an extensive catalogue of severe criticism against an econometric analysis of HP filtered data, the original Hodrick and Prescott (1980, 1997) suggestion to measure the strength of association between (macro-)economic variables by a regression analysis of corresponding HP filtered time series still appears to be popular. A contradictory situation which might be justified only if HP induced distortions were quantitatively negligible in empirical applications. However, this hypothesis can hardly be maintained as the simulation results presented within this paper indicate that HP filtered series give seriously rise to spurious regression results.
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  • Mark Meyer & Peter Winker*, 2005. "Using HP Filtered Data for Econometric Analysis: Some Evidence from Monte Carlo Simulations," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 89(3), pages 303-320, August.
    • Handle: RePEc:spr:alstar:v:89:y:2005:i:3:p:303-320
    DOI: 10.1007/s10182-005-0206-9
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    3. Michael Artis & Toshihiro Okubo, 2008. "The Intranational Business Cycle: Evidence from Japan," Centre for Growth and Business Cycle Research Discussion Paper Series 101, Economics, The University of Manchester.
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    6. Kappler Marcus, 2011. "Business Cycle Co-movement and Trade Intensity in the Euro Area: is there a Dynamic Link?," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(2), pages 247-265, April.
    7. Andreas Brunhart, 2017. "Are Microstates Necessarily Led by Their Bigger Neighbors’ Business Cycle? The Case of Liechtenstein and Switzerland," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 13(1), pages 29-52, May.
    8. Brück, Tilman & Xu, Guo, 2012. "Who gives aid to whom and when? Aid accelerations, shocks and policies," European Journal of Political Economy, Elsevier, vol. 28(4), pages 593-606.
    9. Flaig Gebhard, 2015. "Why We Should Use High Values for the Smoothing Parameter of the Hodrick-Prescott Filter," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 235(6), pages 518-538, December.
    10. Löschel Andreas & Oberndorfer Ulrich, 2009. "Oil and Unemployment in Germany," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 229(2-3), pages 146-162, April.
    11. Auer Benjamin R., 2012. "Lassen sich CAPM, HCAPM und CCAPM durch konsumbasierte zeitvariable Parameterspezifikation rehabilitieren? / Can Time-varying Parameter Specification Based on Consumption Variables Rehabilitate CAPM, ," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 232(5), pages 518-544, October.
    12. João Sousa Andrade & António Portugal Duarte, 2014. "Output-gaps in the PIIGS Economies: An Ingredient of a Greek Tragedy," GEMF Working Papers 2014-06, GEMF, Faculty of Economics, University of Coimbra.
    13. Staszewska-Bystrova Anna, 2013. "Modified Scheffé’s Prediction Bands," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 233(5-6), pages 680-690, October.
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    17. João Sousa Andrade & António Portugal Duarte, 2014. "Output-gaps in the PIIGS Economies: An Ingredient of a Greek Tragedy," GEMF Working Papers 2014-06, GEMF, Faculty of Economics, University of Coimbra.

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    More about this item

    Keywords

    HP filter; spurious regression; detrending; JEL C15; C22;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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