The sensitivity of OLS when variance matrix is (partially) unknown
Banerjee, Anurag N. and Magnus, Jan R. (1999) The sensitivity of OLS when variance matrix is (partially) unknown. Journal of Econometrics, 92, (2), 295-323. (doi:10.1016/S0304-4076(98)00093-1).
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We consider the standard linear regression model y=Xβ+u with all standard assumptions, except that the variance matrix is assumed to be σ2Ω(θ), where Ω depends on m unknown parameters θ1,…, θm. Our interest lies exclusively in the mean parameters β or Xβ. We introduce a new sensitivity statistic (B1) which is designed to decide whether y (or B) is sensitive to covariance misspecification. We show that the Durbin–Watson test is inappropriate in this context, because it measures the sensitivity of Image to covariance misspecification. Our results demonstrate that the estimator Image and the predictor Image are not very sensitive to covariance misspecification. The statistic is easy to use and performs well even in cases where it is not strictly applicable.
|Keywords:||linear regression, least squares, autocorrelation, durbin–watson test, sensitivity|
|Subjects:||H Social Sciences > H Social Sciences (General)
H Social Sciences > HB Economic Theory
H Social Sciences > HA Statistics
|Divisions:||University Structure - Pre August 2011 > School of Social Sciences > Economics
|Date Deposited:||26 Jul 2006|
|Last Modified:||06 Aug 2015 02:30|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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