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Nonlinear models for strongly dependent processes with financial applications

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  • Baillie, Richard T.
  • Kapetanios, George

Abstract

This paper is motivated by recent evidence that many univariate economic and financial time series have both nonlinear and long memory characteristics. Hence, this paper considers a general nonlinear, smooth transition regime autoregression which is embedded within a strongly dependent, long memory process. A time domain MLE with simultaneous estimation of the long memory, linear AR and nonlinear parameters is shown to have desirable asymptotic properties. The Bayesian and Hannan-Quinn information criteria are shown to provide consistent model selection procedures. The paper also considers an alternative two step estimator where the original time series is fractionally filtered from an initial semi-parametric estimate of the long memory parameter. Simulation evidence indicates that the time domain MLE is generally superior to the two step estimator. The paper also includes some applications of the methodology and estimation of a fractionally integrated, nonlinear autoregressive-ESTAR model to forward premium and real exchange rates.

Suggested Citation

  • Baillie, Richard T. & Kapetanios, George, 2008. "Nonlinear models for strongly dependent processes with financial applications," Journal of Econometrics, Elsevier, vol. 147(1), pages 60-71, November.
  • Handle: RePEc:eee:econom:v:147:y:2008:i:1:p:60-71
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. 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.
    2. Baillie Richard T. & Kapetanios George, 2016. "On the estimation of short memory components in long memory time series models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(4), pages 365-375, September.
    3. repec:eee:ecmode:v:70:y:2018:i:c:p:310-319 is not listed on IDEAS
    4. Baillie, Richard T. & Kapetanios, George & Papailias, Fotis, 2014. "Bandwidth selection by cross-validation for forecasting long memory financial time series," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 129-143.
    5. Chatzikonstanti, Vasiliki & Venetis, Ioannis A., 2015. "Long memory in log-range series: Do structural breaks matter?," Journal of Empirical Finance, Elsevier, vol. 33(C), pages 104-113.
    6. Karanasos, M. & Koutroumpis, P. & Karavias, Y. & Kartsaklas, A. & Arakelian, V., 2016. "Inflation convergence in the EMU," Journal of Empirical Finance, Elsevier, vol. 39(PB), pages 241-253.
    7. Baillie, Richard T. & Kongcharoen, Chaleampong & Kapetanios, George, 2012. "Prediction from ARFIMA models: Comparisons between MLE and semiparametric estimation procedures," International Journal of Forecasting, Elsevier, vol. 28(1), pages 46-53.
    8. Hassler, Uwe, 2012. "Impulse responses of antipersistent processes," Economics Letters, Elsevier, vol. 116(3), pages 454-456.
    9. Ulrike Busch & Dieter Nautz, 2010. "Controllability and Persistence of Money Market Rates along the Yield Curve: Evidence from the Euro Area," German Economic Review, Verein für Socialpolitik, vol. 11, pages 367-380, August.
    10. Vozlyublennaia, Nadia, 2011. "The cross-section of dynamics in idiosyncratic risk," Journal of Empirical Finance, Elsevier, vol. 18(3), pages 461-473, June.
    11. Baillie, Richard T. & Kapetanios, George & Papailias, Fotis, 2014. "Modified information criteria and selection of long memory time series models," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 116-131.

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