On the asymptotic expansions for the bias and covariance matrix of autoregressive estimators

1993 ◽  
pp. 80-100
Author(s):  
T. D. Pham
Keyword(s):  
Covariance Matrix ◽  
Asymptotic Expansions

scholarly journals Momentary Lapses: Moment Expansions and the Robustness of Minimum Distance Estimation

1994 ◽  
Vol 10 (1) ◽  
pp. 172-197 ◽  
Author(s):  
Roger Koenker ◽  
José A.F. Machado ◽  
Christopher L. Skeels ◽  
Alan H. Welsh
Keyword(s):  
Least Squares ◽  
Covariance Matrix ◽  
Asymptotic Expansions ◽  
Minimum Distance ◽  
Distance Estimation ◽  
Covariance Matrix Estimation ◽  
Minimum Distance Estimation ◽  
Matrix Estimation ◽  
Error Distributions

This paper explores the robustness of minimum distance (GMM) estimators focusing particularly on the effect of intermediate covariance matrix estimation on final estimator performance. Asymptotic expansions to order Op(n−3/2) are employed to construct O(n−2) expansions for the variance of estimators constructed from preliminary least-squares and general M-estimators. In the former case, there is a rather curious robustifying effect due to estimation of the Eicker-White covariance matrix for error distributions with sufficiently large kurtosis.

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