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Asymptotic normality of pseudo-LS estimator for partly linear autoregression models

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  • Gao, Jiti
  • Liang, Hua

Abstract

Consider the model Yt = [beta]Yt-1 + g(Yt-2) + [var epsilon]t for t [greater-or-equal, slanted] 3. Here g is an unknown function, [beta] is an unknown parameter to be estimated and [var epsilon]t are i.i.d. random error with zero 0 and variance [sigma]2 and [var epsilon]t are independent of Ys for all t [greater-or-equal, slanted] 3 and s = 1, 2. A class of asymptotically normal estimators of [beta] are directly obtained based on piecewise polynomial approximator of g and the model . The asymptotic normality of pseudo-LS (PLS) estimator of [beta] and an estimator of [sigma]2 are investigated.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:stapro:v:23:y:1995:i:1:p:27-34
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    References listed on IDEAS

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    1. Robinson, Peter M, 1988. "Root- N-Consistent Semiparametric Regression," Econometrica, Econometric Society, vol. 56(4), pages 931-954, July.
    2. Hua, Liang & Ping, Cheng, 1993. "Second order asymptotic efficiency in a partial linear model," Statistics & Probability Letters, Elsevier, vol. 18(1), pages 73-84, August.
    3. Rice, John, 1986. "Convergence rates for partially splined models," Statistics & Probability Letters, Elsevier, vol. 4(4), pages 203-208, June.
    4. 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.
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    Cited by:

    1. Cai, Zongwu & Fan, Jianqing, 2000. "Average Regression Surface for Dependent Data," Journal of Multivariate Analysis, Elsevier, vol. 75(1), pages 112-142, October.

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