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Data-driven P-Splines under short-range dependence

Author

Listed:
  • Sebastian Letmathe

    (Paderborn University)

Abstract

This paper focuses on data-driven selection of the smoothing parameter in P-splines for time series with short-range dependence. Well-known asymptotic results of Psplines are first adapted to the current context. A fully automatic iterative plug-in (IPI) algorithm for P-splines is investigated in a comprehensive simulation study. Practical relevance of the IPI is shown by application to economic time series. Moreover, it is illustrated that the IPI can be used for automatic selection of the smoothing parameter of the Hodrick-Prescott filter. Furthermore, a P-spline Log-ACD model is proposed and applied to average daily trade duration data. Smoothing parameter selection is carried via the proposed IPI-algorithm, which performs very well in this context too.

Suggested Citation

  • Sebastian Letmathe, 2022. "Data-driven P-Splines under short-range dependence," Working Papers CIE 152, Paderborn University, CIE Center for International Economics.
    • Handle: RePEc:pdn:ciepap:152
    as

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    File URL: http://groups.uni-paderborn.de/wp-wiwi/RePEc/pdf/ciepap/WP152.pdf
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    References listed on IDEAS

    as
    1. Flaig Gebhard, 2015. "Why We Should Use High Values for the Smoothing Parameter of the Hodrick-Prescott Filter," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 235(6), pages 518-538, December.
    2. Robert F. Engle & Jeffrey R. Russell, 1998. "Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data," Econometrica, Econometric Society, vol. 66(5), pages 1127-1162, September.
    3. Gerda Claeskens & Tatyana Krivobokova & Jean D. Opsomer, 2009. "Asymptotic properties of penalized spline estimators," Biometrika, Biometrika Trust, vol. 96(3), pages 529-544.
    4. Zhang, Michael Yuanjie & Russell, Jeffrey R. & Tsay, Ruey S., 2001. "A nonlinear autoregressive conditional duration model with applications to financial transaction data," Journal of Econometrics, Elsevier, vol. 104(1), pages 179-207, August.
    5. Sebastian Letmathe & Yuanhua Feng, 2022. "An iterative plug-in algorithm for P-Spline regression," Working Papers CIE 151, Paderborn University, CIE Center for International Economics.
    6. Jan Beran & Yuanhua Feng & Sucharita Ghosh, 2015. "Modelling long-range dependence and trends in duration series: an approach based on EFARIMA and ESEMIFAR models," Statistical Papers, Springer, vol. 56(2), pages 431-451, May.
    7. Fernandes, Marcelo & Grammig, Joachim, 2006. "A family of autoregressive conditional duration models," Journal of Econometrics, Elsevier, vol. 130(1), pages 1-23, January.
    8. Luc Bauwens & Pierre Giot, 2000. "The Logarithmic ACD Model: An Application to the Bid-Ask Quote Process of Three NYSE Stocks," Annals of Economics and Statistics, GENES, issue 60, pages 117-149.
    9. Yuanhua Feng & Thomas Gries & Marlon Fritz, 2020. "Data-driven local polynomial for the trend and its derivatives in economic time series," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 32(2), pages 510-533, April.
    10. repec:adr:anecst:y:2000:i:60:p:05 is not listed on IDEAS
    11. Krivobokova, Tatyana & Kauermann, Goran, 2007. "A Note on Penalized Spline Smoothing With Correlated Errors," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1328-1337, December.
    Full references (including those not matched with items on IDEAS)

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    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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