The effects of autocorrelation among errors on the consistency property of OLS variance estimator
type: Item
instance of: scholarly article
title: The effects of autocorrelation among errors on the consistency property of OLS variance estimator
zbMATH Open document ID: 0575.62063
DOI: 10.1016/0304-4076(85)90010-7
author: S. C. Sharma
published in: Journal of Econometrics
publication date: 1985-01-01T00:00:00Z
review text: In a linear regression model the ordinary least squares (OLS) variance estimator \((S^ 2)\) converges in probability to \(E(S^ 2)\) even when the errors are autocorrelated. Of interest, however, is the rate of convergence. In this paper we shed some light on this question for the case of a linear trend model. In particular the relation between the rate of convergence and the correlation property of the errors is explored. It is shown that the retardation of the rate of convergence is not appreciable if the correlation is moderate, but it can be severe for extreme correlations.
Mathematics Subject Classification ID: 62J05
Mathematics Subject Classification ID: 62P20
zbMATH DE Number: 3919584
zbMATH Keywords: effects of autocorrelation among errors
zbMATH Keywords: consistency
zbMATH Keywords: ordinary least squares
zbMATH Keywords: variance estimator
zbMATH Keywords: rate of convergence
zbMATH Keywords: linear trend model
MaRDI profile type: Publication
full work available at URL: 0304-4076(85)90010-7
OpenAlex ID: W1968067169
cites work: Q3847819
cites work: Q5621854
cites work: Q4124141
cites work: Q5556138
cites work: Bounds on the Variance of Regression Coefficients Due to Heteroscedastic or Autoregressive Errors
description: scientific article; zbMATH DE number 3919584