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The local quadratic trend model

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  • Andrew Harvey

    (Faculty of Economics and Politics, University of Cambridge, UK)

Abstract

The local quadratic trend model provides a flexible response to underlying movements in a macroeconomic time series in its estimates of level and change. If the underlying movements are thought of as a trend plus cycle, an estimate of the cycle may be obtained from the quadratic term. Estimating the cycle in this way may offer a useful alternative to other model-based methods of signal extraction, particularly when the series is short. The properties of the filter used to extract the cycle are analysed in the frequency domain and the technique is illustrated with macroeconomic time series from several countries. Copyright © 2009 John Wiley & Sons, Ltd.

Suggested Citation

  • Andrew Harvey, 2010. "The local quadratic trend model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 94-108.
    • Handle: RePEc:jof:jforec:v:29:y:2010:i:1-2:p:94-108
    DOI: 10.1002/for.1144
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    References listed on IDEAS

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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. 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.
    3. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    4. Koopman, Siem Jan & Harvey, Andrew, 2003. "Computing observation weights for signal extraction and filtering," Journal of Economic Dynamics and Control, Elsevier, vol. 27(7), pages 1317-1333, May.
    5. Harvey, Andrew C. & Trimbur, Thomas M. & Van Dijk, Herman K., 2007. "Trends and cycles in economic time series: A Bayesian approach," Journal of Econometrics, Elsevier, vol. 140(2), pages 618-649, October.
    6. Gomez, Victor, 2001. "The Use of Butterworth Filters for Trend and Cycle Estimation in Economic Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(3), pages 365-373, July.
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    Cited by:

    1. José Luis Cendejas & Félix-Fernando Muñoz & Nadia Fernández-de-Pinedo, 2017. "A contribution to the analysis of historical economic fluctuations (1870–2010): filtering, spurious cycles, and unobserved component modeling," Cliometrica, Springer;Cliometric Society (Association Francaise de Cliométrie), vol. 11(1), pages 93-125, January.
    2. Hall, Viv B & Thomson, Peter, 2022. "A boosted HP filter for business cycle analysis: evidence from New Zealand’s small open economy," Working Paper Series 9473, Victoria University of Wellington, School of Economics and Finance.
    3. Cendejas, José Luis & Castañeda, Juan E. & Muñoz, Félix-Fernando, 2014. "Business cycle, interest rate and money in the euro area: A common factor model," Economic Modelling, Elsevier, vol. 43(C), pages 136-141.
    4. Razek, Noha H.A. & Michieka, Nyakundi M., 2019. "OPEC and non-OPEC production, global demand, and the financialization of oil," Research in International Business and Finance, Elsevier, vol. 50(C), pages 201-225.
    5. Viv B. Hall & Peter Thomson, 2022. "A boosted HP filter for business cycle analysis:evidence from New Zealand's small open economy," CAMA Working Papers 2022-45, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    6. Yoon, Gawon, 2015. "Locating change-points in Hodrick–Prescott trends with an application to US real GDP: A generalized unobserved components model approach," Economic Modelling, Elsevier, vol. 45(C), pages 136-141.

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