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The Hodrick-Prescott Filter with a Time-Varying Penalization Parameter. An Application for the Trend Estimation of Global Temperature

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  • Andreas Blöchl
  • Gebhard Flaig

Abstract

In this paper we use the Hodrick-Prescott filter for analysing global temperature data. We are especially concerned with a reliable estimation of the trend component at the end of the data sample. To this end we employ time-varying values for the penalization parameter. The optimal values are derived by a comparison with an ideal filter. The method is applied to temperature data for the northern hemisphere from 1850 to 2012. The main result is that for the optimal specification of the flexible penalization the trend component of temperature is still increasing, possibly with a somewhat lower pace.

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File URL: http://www.cesifo-group.de/portal/page/portal/DocBase_Content/WP/WP-CESifo_Working_Papers/wp-cesifo-2014/wp-cesifo-2014-01/cesifo1_wp4577.pdf
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Bibliographic Info

Paper provided by CESifo Group Munich in its series CESifo Working Paper Series with number 4577.

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Date of creation: 2014
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Handle: RePEc:ces:ceswps:_4577

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Keywords: climate change; global warming; trend; Hodrick-Prescott filter; flexible penalization;

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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. McElroy, Tucker, 2008. "Matrix Formulas For Nonstationary Arima Signal Extraction," Econometric Theory, Cambridge University Press, vol. 24(04), pages 988-1009, August.
  3. Gourieroux,Christian & Monfort,Alain, 1997. "Time Series and Dynamic Models," Cambridge Books, Cambridge University Press, number 9780521411462, October.
  4. Gourieroux,Christian & Monfort,Alain, 1997. "Time Series and Dynamic Models," Cambridge Books, Cambridge University Press, number 9780521423083, October.
  5. Osborn, Denise R, 1995. "Moving Average Detrending and the Analysis of Business Cycles," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 57(4), pages 547-58, November.
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