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On smoothing macroeconomic time series using the modified HP filter

Author

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  • M. Ali Choudhary
  • M. Nadim Hanif
  • Javed Iqbal

Abstract

In business-cycle research, smoothing data is an essential first step to evaluate the extent to which model-generated moments stand up to their empirical counterparts. We put to test McDermott's (1997) modified version of Hodrick and Prescott's (1997) smoothing filter. On the one hand, our simulations suggest that relative to other filters, the modified HP-filter replicates better artificially generated series with known properties. On the other hand, using true data we find that autoregressive properties of smoothed series are not affected by the choice of smoothing HP filters, but the same does not hold when it comes to multivariate analysis. The later result is especially strong for annual data. We report results for a large set of countries.

Suggested Citation

  • M. Ali Choudhary & M. Nadim Hanif & Javed Iqbal, 2014. "On smoothing macroeconomic time series using the modified HP filter," Applied Economics, Taylor & Francis Journals, vol. 46(19), pages 2205-2214, July.
  • Handle: RePEc:taf:applec:v:46:y:2014:i:19:p:2205-2214
    DOI: 10.1080/00036846.2014.894631
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    References listed on IDEAS

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    1. Albert Marcet & Morte O. Ravn, "undated". "The HP-Filter in Cross-Country Comparisons," Studies on the Spanish Economy 100, FEDEA.
    2. Agenor, Pierre-Richard & McDermott, C John & Prasad, Eswar S, 2000. "Macroeconomic Fluctuations in Developing Countries: Some Stylized Facts," The World Bank Economic Review, World Bank, vol. 14(2), pages 251-285, May.
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    Cited by:

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    3. Zhang, Yixin & Zhao, Feng & Zhang, Jie & Wang, Zhicheng, 2021. "Fluctuation in the transformation of economic development and the coupling mechanism with the environmental quality of resource-based cities – A case study of Northeast China," Resources Policy, Elsevier, vol. 72(C).
    4. Adam Pigoń & Michał Ramsza, 2022. "A Comparison of German, Swiss, and Polish Fiscal Rules Using Monte Carlo Simulations," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 4, pages 17-41.
    5. Clegg, Matthew & Krauss, Christopher & Rende, Jonas, 2017. "partialCI: An R package for the analysis of partially cointegrated time series," FAU Discussion Papers in Economics 05/2017, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.

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