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Revisiting simple linear regression with autocorrelated errors

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  • Jaechoul Lee

Abstract

This paper studies properties of ordinary and generalised least squares estimators in a simple linear regression with stationary autocorrelated errors. Explicit expressions for the variances of the regression parameter estimators are derived for some common time series autocorrelation structures, including a first-order autoregression and general moving averages. Applications of the results include confidence intervals and an example where the variance of the trend slope estimator does not increase with increasing autocorrelation. Copyright Biometrika Trust 2004, Oxford University Press.

Suggested Citation

  • Jaechoul Lee, 2004. "Revisiting simple linear regression with autocorrelated errors," Biometrika, Biometrika Trust, vol. 91(1), pages 240-245, March.
    • Handle: RePEc:oup:biomet:v:91:y:2004:i:1:p:240-245
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    Cited by:

    1. Yuzhu Tian & Manlai Tang & Yanchao Zang & Maozai Tian, 2018. "Quantile regression for linear models with autoregressive errors using EM algorithm," Computational Statistics, Springer, vol. 33(4), pages 1605-1625, December.
    2. Mahmoud MOURAD, 2021. "Impact of Socio-Economic Variables on Life Expectancy: An Empirical Study for 138 Countries," Journal of Public Administration and Governance, Macrothink Institute, vol. 11(1), pages 330346-3303, December.
    3. Paolo Maranzano & Matteo Maria Pelagatti, 2022. "Spatio-temporal Event Studies for Air Quality Assessment under Cross-sectional Dependence," Papers 2210.17529, arXiv.org.
    4. Ko, Kyungduk & Lee, Jaechoul & Lund, Robert, 2008. "Confidence intervals for long memory regressions," Statistics & Probability Letters, Elsevier, vol. 78(13), pages 1894-1902, September.
    5. Erum Toor & Tanweer Ul Islam, 2019. "Power Comparison of Autocorrelation Tests in Dynamic Models," International Econometric Review (IER), Econometric Research Association, vol. 11(2), pages 58-69, September.

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