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A new time-varying model for forecasting long-memory series

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  • Luisa Bisaglia

    (University of Padova)

  • Matteo Grigoletto

    (University of Padova)

Abstract

In this work we propose a new class of long-memory models with time-varying fractional parameter. In particular, the dynamics of the long-memory coefficient, d, is specified through a stochastic recurrence equation driven by the score of the predictive likelihood, as suggested by Creal et al. (J Appl Econom 28:777–795, 2013) and Harvey (Dynamic models for volatility and heavy tails: with applications to financial and economic time series, Cambridge University Press, Cambridge, 2013). We demonstrate the validity of the proposed model by a Monte Carlo experiment and an application to two real time series.

Suggested Citation

  • Luisa Bisaglia & Matteo Grigoletto, 2021. "A new time-varying model for forecasting long-memory series," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(1), pages 139-155, March.
    • Handle: RePEc:spr:stmapp:v:30:y:2021:i:1:d:10.1007_s10260-020-00517-7
    DOI: 10.1007/s10260-020-00517-7
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    1. Andersen T. G & Bollerslev T. & Diebold F. X & Labys P., 2001. "The Distribution of Realized Exchange Rate Volatility," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 42-55, March.
    2. Raffaella Giacomini & Halbert White, 2006. "Tests of Conditional Predictive Ability," Econometrica, Econometric Society, vol. 74(6), pages 1545-1578, November.
    3. Ing, Ching-Kang & Wei, Ching-Zong, 2003. "On same-realization prediction in an infinite-order autoregressive process," Journal of Multivariate Analysis, Elsevier, vol. 85(1), pages 130-155, April.
    4. Philipp Sibbertsen, 2004. "Long memory versus structural breaks: An overview," Statistical Papers, Springer, vol. 45(4), pages 465-515, October.
    5. James E. Matheson & Robert L. Winkler, 1976. "Scoring Rules for Continuous Probability Distributions," Management Science, INFORMS, vol. 22(10), pages 1087-1096, June.
    6. Delle Monache, Davide & Petrella, Ivan, 2017. "Adaptive models and heavy tails with an application to inflation forecasting," International Journal of Forecasting, Elsevier, vol. 33(2), pages 482-501.
    7. William Rea & Marco Reale & Jennifer Brown, 2011. "Long memory in temperature reconstructions," Climatic Change, Springer, vol. 107(3), pages 247-265, August.
    8. Blasques, F. & Gorgi, P. & Koopman, S.J., 2021. "Missing observations in observation-driven time series models," Journal of Econometrics, Elsevier, vol. 221(2), pages 542-568.
    9. Cotter, John, 2004. "Absolute Return Volatility," MPRA Paper 3529, University Library of Munich, Germany, revised 2005.
    10. Harvey,Andrew C., 2013. "Dynamic Models for Volatility and Heavy Tails," Cambridge Books, Cambridge University Press, number 9781107630024.
    11. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    12. Francis X. Diebold, 2015. "Comparing Predictive Accuracy, Twenty Years Later: A Personal Perspective on the Use and Abuse of Diebold-Mariano Tests," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(1), pages 1-1, January.
    13. Granger, Clive W. J. & Ding, Zhuanxin, 1996. "Varieties of long memory models," Journal of Econometrics, Elsevier, vol. 73(1), pages 61-77, July.
    14. Uwe Hassler & Barbara Meller, 2014. "Detecting multiple breaks in long memory the case of U.S. inflation," Empirical Economics, Springer, vol. 46(2), pages 653-680, March.
    15. Mohamed Boutahar & Gilles Dufrénot & Anne Péguin-Feissolle, 2008. "A Simple Fractionally Integrated Model with a Time-varying Long Memory Parameter d t," Computational Economics, Springer;Society for Computational Economics, vol. 31(3), pages 225-241, April.
    16. Diks, Cees & Panchenko, Valentyn & van Dijk, Dick, 2011. "Likelihood-based scoring rules for comparing density forecasts in tails," Journal of Econometrics, Elsevier, vol. 163(2), pages 215-230, August.
    17. Roueff, Francois & von Sachs, Rainer, 2011. "Locally stationary long memory estimation," LIDAM Reprints ISBA 2011009, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    18. Tilmann Gneiting, 2008. "Editorial: Probabilistic forecasting," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(2), pages 319-321, April.
    19. Anthony Tay & Kenneth F. Wallis, 2000. "Density Forecasting: A Survey," Econometric Society World Congress 2000 Contributed Papers 0370, Econometric Society.
    20. Bonnie K. Ray & Ruey S. Tsay, 2002. "Bayesian methods for change‐point detection in long‐range dependent processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 23(6), pages 687-705, November.
    21. Tilmann Gneiting & Fadoua Balabdaoui & Adrian E. Raftery, 2007. "Probabilistic forecasts, calibration and sharpness," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(2), pages 243-268, April.
    22. Tilmann Gneiting & Roopesh Ranjan, 2011. "Comparing Density Forecasts Using Threshold- and Quantile-Weighted Scoring Rules," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(3), pages 411-422, July.
    23. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
    24. Massimiliano Caporin & Juliusz Preś, 2013. "Forecasting Temperature Indices Density with Time‐Varying Long‐Memory Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(4), pages 339-352, July.
    25. Gneiting, Tilmann & Ranjan, Roopesh, 2011. "Comparing Density Forecasts Using Threshold- and Quantile-Weighted Scoring Rules," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(3), pages 411-422.
    26. Reinhard Selten, 1998. "Axiomatic Characterization of the Quadratic Scoring Rule," Experimental Economics, Springer;Economic Science Association, vol. 1(1), pages 43-61, June.
    27. Roueff, François & von Sachs, Rainer, 2011. "Locally stationary long memory estimation," Stochastic Processes and their Applications, Elsevier, vol. 121(4), pages 813-844, April.
    28. C. W. J. Granger & Roselyne Joyeux, 1980. "An Introduction To Long‐Memory Time Series Models And Fractional Differencing," Journal of Time Series Analysis, Wiley Blackwell, vol. 1(1), pages 15-29, January.
    29. repec:hal:journl:peer-00834423 is not listed on IDEAS
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    1. Magda Monteiro & Marco Costa, 2023. "Change Point Detection by State Space Modeling of Long-Term Air Temperature Series in Europe," Stats, MDPI, vol. 6(1), pages 1-18, January.

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