The efficiency of OLS in the presence of auto-correlated disturbances in regression models

Samir Safi, Alexander White

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

The ordinary least squares (OLS) estimates in the regression model are efficient when the disturbances have mean zero, constant variance, and are uncorrelated. In problems concerning time series, it is often the case that the disturbances are correlated. Using computer simulations, the robustness of various estimators are considered, including estimated generalized least squares. It was found that if the disturbance structure is autoregressive and the dependent variable is nonstochastic and linear or quadratic, the OLS performs nearly as well as its competitors. For other forms of the dependent variable, rules of thumb are presented to guide practitioners in the choice of estimators.

Original languageEnglish
Pages (from-to)106-116
Number of pages11
JournalJournal of Modern Applied Statistical Methods
Volume5
Issue number1
DOIs
Publication statusPublished - 2006
Externally publishedYes

Keywords

  • Autocorrelation
  • Autoregressive
  • Efficiency
  • Generalized least squares
  • Ordinary least squares

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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