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Adaptive pointwise estimation in time-inhomogeneous time-series models

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  • Pavel Cizek
  • Wolfgang Härdle
  • Vladimir Spokoiny

Abstract

This paper offers a new method for estimation and forecasting of the linear and nonlinear time series when the stationarity assumption is violated. Our general local parametric approach particularly applies to general varying-coefficient parametric models, such as AR or GARCH, whose coefficients may arbitrarily vary with time. Global parametric, smooth transition, and changepoint models are special cases. The method is based on an adaptive pointwise selection of the largest interval of homogeneity with a given right-end point by a local change-point analysis. We construct locally adaptive estimates that can perform this task and investigate them both from the theoretical point of view and by Monte Carlo simulations. In the particular case of GARCH estimation, the proposed method is applied to stock-index series and is shown to outperform the standard parametric GARCH model.

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Bibliographic Info

Paper provided by Sonderforschungsbereich 649, Humboldt University, Berlin, Germany in its series SFB 649 Discussion Papers with number SFB649DP2008-002.

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Length: 47 pages
Date of creation: Jan 2008
Date of revision:
Handle: RePEc:hum:wpaper:sfb649dp2008-002

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Keywords: adaptive pointwise estimation; autoregressive models; conditional heteroscedasticity models; local time-homogeneity;

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References

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Citations

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Cited by:
  1. Matthias R. Fengler & Ostap Okhrin, 2012. "Realized Copula," SFB 649 Discussion Papers SFB649DP2012-034, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  2. Anna Louise Schroeder & Piotr Fryzlewicz, 2013. "Adaptive trend estimation in financial time series via multiscale change-point-induced basis recovery," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library 54934, London School of Economics and Political Science, LSE Library.
  3. Giammarino, Flavia & Barrieu, Pauline, 2009. "A semiparametric model for the systematic factors of portfolio credit risk premia," Journal of Empirical Finance, Elsevier, Elsevier, vol. 16(4), pages 655-670, September.

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