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Some Alternatives for Robust Estimation of the Spectrum in Stationary Processes

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  • Fajardo, Fabio Alexander

Abstract

This paper is dedicated to estimation of the spectral density of stationary linear processes in the presence of additive outliers. We suggest the use of robust periodogram proposed by Fajardo et. al. (2009) (LPR) with different smoothing windows. Empirical results showed the robustness of the estimator under additive outliers. A real data application is presented with IGP-DI series.

Suggested Citation

  • Fajardo, Fabio Alexander, 2011. "Some Alternatives for Robust Estimation of the Spectrum in Stationary Processes," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 31(1), March.
  • Handle: RePEc:sbe:breart:v:31:y:2011:i:1:a:2767
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    1. Gemai Chen & Bovas Abraham & Shelton Peiris, 1994. "Lag Window Estimation Of The Degree Of Differencing In Fractionally Integrated Time Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 15(5), pages 473-487, September.
    2. Haldrup, Niels & Nielsen, Morten Orregaard, 2007. "Estimation of fractional integration in the presence of data noise," Computational Statistics & Data Analysis, Elsevier, vol. 51(6), pages 3100-3114, March.
    3. Deo, Rohit S. & Hurvich, Clifford M., 2001. "On The Log Periodogram Regression Estimator Of The Memory Parameter In Long Memory Stochastic Volatility Models," Econometric Theory, Cambridge University Press, vol. 17(4), pages 686-710, August.
    4. Ledolter, Johannes, 1989. "The effect of additive outliers on the forecasts from ARIMA models," International Journal of Forecasting, Elsevier, vol. 5(2), pages 231-240.
    5. Kaizô I. BeltraTo & Peter Bloomfield, 1987. "Determining The Bandwidth Of A Kernel Spectrum Estimate," Journal of Time Series Analysis, Wiley Blackwell, vol. 8(1), pages 21-38, January.
    6. Clifford M. Hurvich & Rohit Deo & Julia Brodsky, 1998. "The mean squared error of Geweke and Porter‐Hudak's estimator of the memory parameter of a long‐memory time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 19(1), pages 19-46, January.
    7. Céline Lévy‐Leduc & Hélène Boistard & Eric Moulines & Murad S. Taqqu & Valderio A. Reisen, 2011. "Robust estimation of the scale and of the autocovariance function of Gaussian short‐ and long‐range dependent processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 32(2), pages 135-156, March.
    8. Yanyuan Ma & Marc G. Genton, 2000. "Highly Robust Estimation of the Autocovariance Function," Journal of Time Series Analysis, Wiley Blackwell, vol. 21(6), pages 663-684, November.
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