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Estimation of the autocorrelation coefficient in the presence of a regression trend

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  • Schick, Anton

Abstract

In this paper estimation of the autocorrelation parameter [sigma] is studied in nonparametric and semiparametric regression models with autoregressive errors. It is shown that under mild assumptions one can construct an estimator that is asymptotically equivalent to the least squares estimator based on the autoregressive error process.

Suggested Citation

  • Schick, Anton, 1994. "Estimation of the autocorrelation coefficient in the presence of a regression trend," Statistics & Probability Letters, Elsevier, vol. 21(5), pages 371-380, December.
    • Handle: RePEc:eee:stapro:v:21:y:1994:i:5:p:371-380
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    Cited by:

    1. Orlandini, E. & Gonnella, G. & Yeomans, J.M., 1997. "Lattice Boltzmann study of spinodal decomposition in structured fluids," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 240(1), pages 277-285.
    2. Schick, Anton, 1996. "Efficient estimation in a semiparametric additive regression model with autoregressive errors," Stochastic Processes and their Applications, Elsevier, vol. 61(2), pages 339-361, February.
    3. You, Jinhong & Sun, Xiaoqian & Pang, Wan-kai & Leung, Ping-kei, 2002. "Jackknifing type weighted least squares estimators in partially linear regression models," Statistics & Probability Letters, Elsevier, vol. 60(1), pages 17-31, November.
    4. Liang, Han-Ying & Fan, Guo-Liang, 2009. "Berry-Esseen type bounds of estimators in a semiparametric model with linear process errors," Journal of Multivariate Analysis, Elsevier, vol. 100(1), pages 1-15, January.

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