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Econometric Filters

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  • D.S.G. Pollock

Abstract

A variety of filters that are commonly employed by econometricians are analysed with a view to determining their effectiveness in extracting well-defined components of economic data sequences. These components can be defined in terms of their spectral structures—i.e. their frequency content—and it is argued that the process of econometric signal extraction should be guided by a careful appraisal of the periodogram of the detrended data sequence. Whereas it is true that many annual and quarterly economic data sequences are amenable to relatively unsophisticated filtering techniques, it is often the case that monthly data that exhibit strong seasonal fluctuations require a far more delicate approach. In such cases, it may be appropriate to use filters that work directly in the frequency domain by selecting or modifying the spectral ordinates of a Fourier decomposition of data that have been subject to a preliminary detrending.

Suggested Citation

  • D.S.G. Pollock, 2017. "Econometric Filters," Discussion Papers in Economics 17/01, Division of Economics, School of Business, University of Leicester.
  • Handle: RePEc:lec:leecon:17/01
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    1. Marianne Baxter & Robert G. King, 1999. "Measuring Business Cycles: Approximate Band-Pass Filters For Economic Time Series," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 575-593, November.
    2. D. S. G. Pollock, 2016. "Econometric Filters," Computational Economics, Springer;Society for Computational Economics, vol. 48(4), pages 669-691, December.
    3. Hodrick, Robert J & Prescott, Edward C, 1997. "Postwar U.S. Business Cycles: An Empirical Investigation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 29(1), pages 1-16, February.
    4. D.S.G. Pollock, 2008. "Realisations of Finite-Sample Frequency-Selective Filters," Discussion Papers in Economics 08/32, Division of Economics, School of Business, University of Leicester.
    5. Arthur F. Burns & Wesley C. Mitchell, 1946. "Measuring Business Cycles," NBER Books, National Bureau of Economic Research, Inc, number burn46-1.
    6. Diebold, Francis X., 1993. "Discussion : The effect of seasonal adjustment filters on tests for a unit root," Journal of Econometrics, Elsevier, vol. 55(1-2), pages 99-103.
    7. Lawrence J. Christiano & Terry J. Fitzgerald, 2003. "The Band Pass Filter," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 44(2), pages 435-465, May.
    8. Gerhard Bry & Charlotte Boschan, 1971. "Foreword to "Cyclical Analysis of Time Series: Selected Procedures and Computer Programs"," NBER Chapters, in: Cyclical Analysis of Time Series: Selected Procedures and Computer Programs, pages -1, National Bureau of Economic Research, Inc.
    9. Tomas del Barrio Castro & Denise R. Osborn, 2004. "The consequences of seasonal adjustment for periodic autoregressive processes," Econometrics Journal, Royal Economic Society, vol. 7(2), pages 307-321, December.
    10. Pollock, D. S. G., 2000. "Trend estimation and de-trending via rational square-wave filters," Journal of Econometrics, Elsevier, vol. 99(2), pages 317-334, December.
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    Cited by:

    1. Carlos Medel, 2017. "Forecasting Chilean inflation with the hybrid new keynesian Phillips curve: globalisation, combination, and accuracy," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 20(3), pages 004-050, December.
    2. Luis J. Álvarez, 2017. "Business Cycle Estimation with High-Pass and Band-Pass Local Polynomial Regression," Econometrics, MDPI, vol. 5(1), pages 1-11, January.
    3. Carlos A. Medel, 2018. "Forecasting Inflation with the Hybrid New Keynesian Phillips Curve: A Compact-Scale Global VAR Approach," International Economic Journal, Taylor & Francis Journals, vol. 32(3), pages 331-371, July.
    4. D. S. G. Pollock, 2016. "Econometric Filters," Computational Economics, Springer;Society for Computational Economics, vol. 48(4), pages 669-691, December.

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    Keywords

    Time series; Spectral analysis; Business cycles; Turning points; Seasonality.;
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