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Efficiency of Least-Squares Estimation of Linear Trend when Residuals Are Autocorrelated
first-order stationary Markoff process with zero mean and autocorrelation coefficient p, - 1 < p < 1, the greatest lower bound for the efficiency of the least-squares estimator of 8 (relative to the Gauss-Markoff estimator) over the interval 0;p < 1 is .753763. This compares with a greatest lower bound of .535898 for the relative efficiency of the Cochrane-Orcutt estimator of 38.
A Simple Test for Heteroscedasticity and Random Coefficient Variation
A simple test for heteroscedastic disturbances in a linear regression model is developed using the framework of the Lagrangian multiplier test. For a wide range of heteroscedastic and random coefficient specifications, the criterion is given as a readily computed function of the OLS residuals. Some finite sample evidence is presented to supplement the general asymptotic properties of Lagrangian multiplier tests.