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Adaptive Semiparametric Estimation In The Presence Of Autocorrelation Of Unknown Form

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  • F. Javier Hidalgo

Abstract

. In a time series regression model the residual autoregression function is an unknown, possibly non‐linear, function. It is estimated by non‐parametric kernel regression. The resulting least‐squares estimate of the regression function is shown to be adapative, in the sense of having the same asymptotic distribution, to first order, as estimates based on knowledge of the autoregression function. Also, a Monte Carlo experiment about the behaviour of the estimator is described.

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  • F. Javier Hidalgo, 1992. "Adaptive Semiparametric Estimation In The Presence Of Autocorrelation Of Unknown Form," Journal of Time Series Analysis, Wiley Blackwell, vol. 13(1), pages 47-78, January.
    • Handle: RePEc:bla:jtsera:v:13:y:1992:i:1:p:47-78
    DOI: 10.1111/j.1467-9892.1992.tb00094.x
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    Cited by:

    1. Gao, Jiti, 2007. "Nonlinear time series: semiparametric and nonparametric methods," MPRA Paper 39563, University Library of Munich, Germany, revised 01 Sep 2007.
    2. Jiti Gao & Maxwell King, 2011. "A New Test in Parametric Linear Models against Nonparametric Autoregressive Errors," Monash Econometrics and Business Statistics Working Papers 20/11, Monash University, Department of Econometrics and Business Statistics.
    3. Aman Ullah & Tao Wang & Weixin Yao, 2021. "Modal regression for fixed effects panel data," Empirical Economics, Springer, vol. 60(1), pages 261-308, January.
    4. Gao, Jiti & Liang, Hua, 1995. "Asymptotic normality of pseudo-LS estimator for partly linear autoregression models," Statistics & Probability Letters, Elsevier, vol. 23(1), pages 27-34, April.
    5. Pinkse, Joris, 1998. "A consistent nonparametric test for serial independence," Journal of Econometrics, Elsevier, vol. 84(2), pages 205-231, June.

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