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A Simulation Study of Autoregressive and Window Estimators of the Inverse Correlation Function

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  • R. J. Bhansali

Abstract

The results of a simulation study aimed at investigating and comparing the finite sample behaviour of the autoregressive and the window methods of estimating the inverse correlation function are described. Three moving average and one autoregressive processes of second order are considered. The behaviour of these estimators as the order, k, of the fitted autoregression, the bandwidth parameter, m, of the spectral window and the series length, T, are varied is discussed. The usefulness of the asymptotic distribution of these estimators as an approximation to their finite sample distribution is examined.

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  • R. J. Bhansali, 1983. "A Simulation Study of Autoregressive and Window Estimators of the Inverse Correlation Function," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 32(2), pages 141-149, June.
    • Handle: RePEc:bla:jorssc:v:32:y:1983:i:2:p:141-149
    DOI: 10.2307/2347293
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    Cited by:

    1. R. H. Glendinning, 2000. "Estimating the Inverse Autocorrelation Function from Outlier Contaminated Data," Computational Statistics, Springer, vol. 15(4), pages 541-565, December.
    2. Caiado, Jorge & Crato, Nuno & Pena, Daniel, 2006. "A periodogram-based metric for time series classification," Computational Statistics & Data Analysis, Elsevier, vol. 50(10), pages 2668-2684, June.
    3. Andrés Alonso & Daniel Peña & Juan Romo, 2006. "Introducing model uncertainty by moving blocks bootstrap," Statistical Papers, Springer, vol. 47(2), pages 167-179, March.
    4. Roberto Baragona & Francesco Battaglia, 1995. "Linear Interpolators And The Inverse Correlation Function Of Non‐Stationary Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 16(6), pages 531-538, November.
    5. João Henrique Gonçalves Mazzeu & Esther Ruiz & Helena Veiga, 2018. "Uncertainty And Density Forecasts Of Arma Models: Comparison Of Asymptotic, Bayesian, And Bootstrap Procedures," Journal of Economic Surveys, Wiley Blackwell, vol. 32(2), pages 388-419, April.
    6. Alonso Fernández, Andrés Modesto & Romo, Juan, 2001. "Forecast of the expected non-epidemic morbidity of acute diseases using resampling methods," DES - Working Papers. Statistics and Econometrics. WS ws013422, Universidad Carlos III de Madrid. Departamento de Estadística.
    7. Alonso Fernández, Andrés Modesto & Peña, Daniel & Romo, Juan, 2001. "Introducing model uncertainty in time series bootstrap," DES - Working Papers. Statistics and Econometrics. WS ws011409, Universidad Carlos III de Madrid. Departamento de Estadística.

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