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Consequences of Hodrick-Prescott filtering for parameter estimation in a structural model of inventory behaviour

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  • D. Doorn

Abstract

Much work in macroeconomics relies on detrending a time series prior to analysis. A popular method of detrending has been the Hodrick-Prescott (HP) filter. This filter has been widely applied in the Real Business Cycle literature to isolate the behaviour of economic variables at business cycle frequencies and to look at comovements between series over the business cycle. Prior work has shown that the use of this filter can have serious consequences for such analysis, such as inducing spurious correlations, and that a researcher should proceed with caution when applying the filter. Another use of HP filtering has been to achieve stationarity prior to estimation of structural econometric models. Little work has been done concerning the possible effects this method of detrending may have on parameter estimation from such models. Given the problems with the filter noted in the literature, it is likely these effects may be of some consequence to estimation results. Using a common model of inventory behaviour, a simulation study is conducted to assess the impact of using the HP filter for detrending prior to estimation. A comparison will be made to other methods of handling trend to gauge relative performance.

Suggested Citation

  • D. Doorn, 2006. "Consequences of Hodrick-Prescott filtering for parameter estimation in a structural model of inventory behaviour," Applied Economics, Taylor & Francis Journals, vol. 38(16), pages 1863-1875.
    • Handle: RePEc:taf:applec:v:38:y:2006:i:16:p:1863-1875
    DOI: 10.1080/00036840500427254
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    1. Uhlig, H.F.H.V.S. & Ravn, M., 1997. "On Adjusting the H-P Filter for the Frequency of Observations," Discussion Paper 1997-50, Tilburg University, Center for Economic Research.
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    Cited by:

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    3. Xiaoshan Chen & Ronald MacDonald, 2010. "Revisiting the Dollar-Euro Permanent Equilibrium Exchange Rate: Evidence from Multivariate Unobserved Components Models," Working Papers 2010_16, Business School - Economics, University of Glasgow.
    4. Gorodnichenko, Yuriy & Ng, Serena, 2010. "Estimation of DSGE models when the data are persistent," Journal of Monetary Economics, Elsevier, vol. 57(3), pages 325-340, April.

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