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In a linear regression model, when errors are autocorrelated, several asymptotically efficient estimators of parameters have been suggested in the literature. In this paper we study their small sample efficiency using Monte Carlo methods. While none of these estimators turns out to be distinctly superior to the others over the entire range of parameters, there is a definite gain in efficiency to be had from using some two-stage procedure in the presence of moderate high levels of serial correlation in the residuals and very little loss from using such methods when the true rho is small. Where computational costs are a consideration a mixed strategy of switching to a second stage only if the estimated rho is higher than some critical value is suggested and is shown to perform quite well over the whole parameter range. [ABSTRACT FROM AUTHOR]

In this paper the problem of choosing among point estimators on the basis of their small sample properties is discussed from the sampling point of view. The indeterminacy of most Monte Carlo studies is analysed and resolved within the framework of statistical decision theory. A first order autoregressive model is worked through in detail both for its own sake and to illustrate how a complete Monte Carlo study might be done. [ABSTRACT FROM AUTHOR]

The present study investigated the efficacy of prediction of Horst's Absolute and Differential Prediction Methods when applied to within-groups correlation matrices. Two treatments of missing data, employing increases of total group intercorrelation matrices and subgroup intercorrelation matrices were incorporated in both the Absolute and Differential Prediction Methods. The synthesis of these methods generated four composite weighting systems, thus permitting a comparison of the accuracy of predictions resulting from the four systems on the basis of weight validities (cross-validation correlation coefficients). [ABSTRACT FROM AUTHOR]