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A Zero-One Result for the Least Squares Estimator

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Abstract

The least squares estimator for the linear regression model is shown to converge to the true parameter vector either with probability one or with probability zero under weak conditions on the dependent random variable and regressor variables. No additional conditions are placed on the errors. The dependent and regressor variables are assumed to be weakly dependent -- in particular, to be strong mixing. The regressors may be fixed or random and must exhibit a certain degree of independent variability. No further assumptions are needed. The model considered allows the number of regressors to increase without bound as the sample size increases. The proof proceeds by extending Kolmogorov's 0-1 law for independent random variables to strong mixing random variables.

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  • Donald W.K. Andrews, 1984. "A Zero-One Result for the Least Squares Estimator," Cowles Foundation Discussion Papers 698, Cowles Foundation for Research in Economics, Yale University.
    • Handle: RePEc:cwl:cwldpp:698
    Note: CFP 621.
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    Cited by:

    1. Marco Merkli & Mark Penney, 2015. "Quantum Measurements of Scattered Particles," Mathematics, MDPI, vol. 3(1), pages 1-27, March.
    2. repec:esx:essedp:774 is not listed on IDEAS
    3. Gupta, A, 2015. "Nonparametric specification testing via the trinity of tests," Economics Discussion Papers 15619, University of Essex, Department of Economics.
    4. Gupta, Abhimanyu, 2018. "Nonparametric specification testing via the trinity of tests," Journal of Econometrics, Elsevier, vol. 203(1), pages 169-185.
    5. Donald W.K. Andrews, 1986. "On the Performance of Least Squares in Linear Regression with Undefined Error Means," Cowles Foundation Discussion Papers 798, Cowles Foundation for Research in Economics, Yale University.

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