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Regression Coefficient Estimation In Small Linear Models With Unknown Autocorrelation: Evaluation And Recommendation

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  • Dubbelman, C.
  • de Groot, E. A.

Abstract

This study evaluates estimators of the regression coefficients in the linear model, where the disturbances follow a first order autocorrelation scheme with unkriown parameter. Our interest i concentrated on situations where the data matrix consists of n = 15 observations on each of k = 3 regressors from the field of economic time series. Several well-known estimation procedures are compared with regard to efficiency and confidence, by means of simulations. The study terminates in a recommended procedure, which is a blend of OLS, the Prais-Winsten method, and a newly designed method.

Suggested Citation

  • Dubbelman, C. & de Groot, E. A., 1983. "Regression Coefficient Estimation In Small Linear Models With Unknown Autocorrelation: Evaluation And Recommendation," Econometric Institute Archives 272275, Erasmus University Rotterdam.
    • Handle: RePEc:ags:eureia:272275
    DOI: 10.22004/ag.econ.272275
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    References listed on IDEAS

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    1. Stroeker, R. J., 1983. "Approximations of the eigenvalues of the covariance matrix of a first-order autoregressive process," Journal of Econometrics, Elsevier, vol. 22(3), pages 269-279, August.
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