@ARTICLE{16360354_1969,
author = {Griliches, Zvi and Rao, Potluri V.},
keywords = {asymptotic theory, monte carlo method, regression model, асимптотический подход, математический метод, метод Монте-Карло, регрессионная модель, эконометрическая модель, эконометрический метод},
title = {Small-Sample Properties of Several Two-Stage Regression Methods in
the Context of Auto-Correlated Errors },
journal = {Journal of the American Statistical Association},
year = {1969},
month = {},
volume = {64},
number = {325},
pages = {253-272},
url = {http://ecsocman.hse.ru/text/16360354/},
publisher = {},
language = {ru},
abstract = {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 $\hat rho$ is higher than
some critical value is suggested and is shown to perform quite well
over the whole parameter range. },
annote = {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 $\hat rho$ is higher than
some critical value is suggested and is shown to perform quite well
over the whole parameter range. }
}