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Teaching Regressions with a Lagged Dependent Variable and Autocorrelated Disturbances

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  • Asatoshi Maeshiro

Abstract

The author attempts to rectify the unsatisfactory textbook treatment of the finite-sample properties of estimators of regression models with a lagged dependent variable and autocorrelated disturbances. He contends that the bias of the OLS estimator of a regression model with a lagged dependent variable and autocorrelated disturbances is determined by two effects, the dynamic effect and the correlation effect, which may be reinforcing or offsetting. The implications of these two effects are explored within a theoretical and a Monte Carlo framework.

Suggested Citation

  • Asatoshi Maeshiro, 1996. "Teaching Regressions with a Lagged Dependent Variable and Autocorrelated Disturbances," The Journal of Economic Education, Taylor & Francis Journals, vol. 27(1), pages 72-84, January.
    • Handle: RePEc:taf:jeduce:v:27:y:1996:i:1:p:72-84
    DOI: 10.1080/00220485.1996.10844896
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    References listed on IDEAS

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    1. Andrew C. Harvey, 1990. "The Econometric Analysis of Time Series, 2nd Edition," MIT Press Books, The MIT Press, edition 2, volume 1, number 026208189x, April.
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    Cited by:

    1. Kemme, David M. & McInish, Thomas H. & Zhang, Jiang, 2022. "Market fairness and efficiency: Evidence from the Tokyo Stock Exchange," Journal of Banking & Finance, Elsevier, vol. 134(C).
    2. Toni Stocker, 2007. "On the asymptotic bias of OLS in dynamic regression models with autocorrelated errors," Statistical Papers, Springer, vol. 48(1), pages 81-93, January.
    3. Nicolás Cachanosky & Alexandre Padilla, 2020. "A panel data analysis of Latin American populism," Constitutional Political Economy, Springer, vol. 31(3), pages 329-343, September.

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