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Estimation Of Linear Regression Model With Autocorrelated Disturbances

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  • A. Ullah
  • V. K. Srivastava
  • L. Magee
  • A. Srivastava

Abstract

. In this paper we have derived the large sample asymptotic approximation for the variance‐covariance matrix of the two stage Prais‐Winston estimator of the regression coefficients. The efficiency properties of this estimator with respect to ordinary least squares, and generalized least squares with a known autocorrelation coefficient are then analysed numerically. The results are useful for the practitioners dealing with moderate size sample data.

Suggested Citation

  • A. Ullah & V. K. Srivastava & L. Magee & A. Srivastava, 1983. "Estimation Of Linear Regression Model With Autocorrelated Disturbances," Journal of Time Series Analysis, Wiley Blackwell, vol. 4(2), pages 127-135, March.
    • Handle: RePEc:bla:jtsera:v:4:y:1983:i:2:p:127-135
    DOI: 10.1111/j.1467-9892.1983.tb00364.x
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    Cited by:

    1. Miyazaki, Shigetaka & Griffiths, William E., 1984. "The properties of some covariance matrix estimators in linear models with AR(1) errors," Economics Letters, Elsevier, vol. 14(4), pages 351-356.
    2. Dedi Rosadi & Shelton Peiris, 2014. "Second-order least-squares estimation for regression models with autocorrelated errors," Computational Statistics, Springer, vol. 29(5), pages 931-943, October.

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