Home>Publications>Econometrica>Bounds for the Bias of the Least Squares Estimator of @s^2 in the Case of a First-Order Autoregressive Process (Positive Autocorrelation)
This paper considers the least squares estimator of @?^2 in the linear model with disturbances generated by a first-order autoregressive process. It is well known that the estimator is biased. In this paper an attempt is made to establish bounds for the bias. These bounds depend on n, k, and @r, where n is the number of observations, k is the number of parameters, and @r is the (positive) coefficient of the autoregressive process.
MLA
Neudecker, H.. “Bounds for the Bias of the Least Squares Estimator of @s^2 in the Case of a First-Order Autoregressive Process (Positive Autocorrelation).” Econometrica, vol. 45, .no 5, Econometric Society, 1977, pp. 1257-1262, https://www.jstor.org/stable/1914071
Chicago
Neudecker, H.. “Bounds for the Bias of the Least Squares Estimator of @s^2 in the Case of a First-Order Autoregressive Process (Positive Autocorrelation).” Econometrica, 45, .no 5, (Econometric Society: 1977), 1257-1262. https://www.jstor.org/stable/1914071
APA
Neudecker, H. (1977). Bounds for the Bias of the Least Squares Estimator of @s^2 in the Case of a First-Order Autoregressive Process (Positive Autocorrelation). Econometrica, 45(5), 1257-1262. https://www.jstor.org/stable/1914071
We are deeply saddened by the passing of Kate Ho, the John L. Weinberg Professor of Economics and Business Policy at Princeton University and a Fellow of the Econometric Society. Kate was a brilliant IO economist and scholar whose impact on the profession will resonate for many years to come.
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