Asymptotic efficiency of the pseudo-maximum likelihood estimator in multi-group factor models with pooled data

2015 ◽  
Vol 69 (1) ◽  
pp. 20-42 ◽  
Author(s):  
Shaobo Jin ◽  
Fan Yang-Wallentin ◽  
Anders Christoffersson
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Asymptotic Efficiency ◽  
Factor Models ◽  
Group Factor ◽  
Likelihood Estimator ◽  
Pooled Data ◽  
Pseudo Maximum Likelihood

OPTIMAL INVARIANT INFERENCE WHEN THE NUMBER OF INSTRUMENTS IS LARGE

2009 ◽  
Vol 25 (3) ◽  
pp. 793-805 ◽  
Author(s):  
Laura Chioda ◽  
Michael Jansson
Keyword(s):  
Asymptotic Behavior ◽  
Maximum Likelihood ◽  
Sample Size ◽  
Maximum Likelihood Estimator ◽  
Instrumental Variables ◽  
Asymptotic Efficiency ◽  
Rotation Invariance ◽  
Limited Information ◽  
Likelihood Estimator ◽  
Rotation Invariant

This paper studies the asymptotic behavior of a Gaussian linear instrumental variables model in which the number of instruments diverges with the sample size. Asymptotic efficiency bounds are obtained for rotation invariant inference procedures and are shown to be attainable by procedures based on the limited information maximum likelihood estimator. The bounds are obtained by characterizing the limiting experiment associated with the model induced by the rotation invariance restriction.

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