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Convenient Methods for Estimation of Linear Regression Models with MA(1) Errors

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  • Glenn M. MacDonald
  • James G. MacKinnon

Abstract

This paper proposes computationally convenient methods for estimating linear regression models with first-order moving average, or MA(1), error structures. Conditional on alpha, the parameter of the MA(1) process, estimates of the regression coefficients may be obtained by ordinary least squares. Searching over alpha then yields full maximum likelihood estimates. A method of moments estimator for alpha can also be used to obtain less efficient but computationally simpler estimates. The performance of these two estimators is investigated by sampling experiments. An empirical example is presented involving the relationship between GNP and unemployment.

Suggested Citation

  • Glenn M. MacDonald & James G. MacKinnon, 1983. "Convenient Methods for Estimation of Linear Regression Models with MA(1) Errors," Working Paper 540, Economics Department, Queen's University.
    • Handle: RePEc:qed:wpaper:540
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    Cited by:

    1. Joseph Ross, 2021. "Stationarity Statistics on Rolling Windows," Computational Economics, Springer;Society for Computational Economics, vol. 57(2), pages 655-691, February.
    2. Stefan De Wachter & Richard D.F. Harris & Elias Tzavalis, 2005. "Panel Data Unit Roots Tests: The Role of Serial Correlation and the Time Dimension," Working Papers 550, Queen Mary University of London, School of Economics and Finance.
    3. Branch, William & Tilley, Daniel S., 1991. "Catfish Producer Harvest Response To Production And Asymmetric Price Risk," Southern Journal of Agricultural Economics, Southern Agricultural Economics Association, vol. 23(2), pages 1-9, December.
    4. Marcel die Dama & Boniface ngah Epo & Galex syrie Soh, 2013. "Developing a two way error component estimation model with disturbances following a special autoregressive (4) for quarterly data," Economics Bulletin, AccessEcon, vol. 33(1), pages 625-634.
    5. Stefan De Wachter & Richard D.F. Harris & Elias Tzavalis, 2005. "Panel Data Unit Roots Tests: The Role of Serial Correlation and the Time Dimension," Working Papers 550, Queen Mary University of London, School of Economics and Finance.

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