On Score Tests in Structural Regression Models

Statistics ◽  
1998 ◽  
Vol 32 (2) ◽  
pp. 131-149
Author(s):  
Reinaldo B. Arellano-Valle ◽  
Heleno Bolfarine
1989 ◽  
Vol 5 (3) ◽  
pp. 363-384 ◽  
Author(s):  
Russell Davidson ◽  
James G. MacKinnon

We consider several issues related to Durbin-Wu-Hausman tests; that is, tests based on the comparison of two sets of parameter estimates. We first review a number of results about these tests in linear regression models, discuss what determines their power, and propose a simple way to improve power in certain cases. We then show how in a general nonlinear setting they may be computed as “score” tests by means of slightly modified versions of any artificial linear regression that can be used to calculate Lagrange multiplier tests, and explore some of the implications of this result. In particular, we show how to create a variant of the information matrix test that tests for parameter consistency. We examine the conventional information matrix test and our new version in the context of binary-choice models, and provide a simple way to compute both tests using artificial regressions.


2017 ◽  
Vol 33 (4) ◽  
pp. 851-864
Author(s):  
Feng-chang Xie ◽  
Jin-guan Lin ◽  
Bo-cheng Wei

2009 ◽  
Vol 53 (9) ◽  
pp. 3478-3489 ◽  
Author(s):  
Feng-Chang Xie ◽  
Bo-Cheng Wei ◽  
Jin-Guan Lin

2001 ◽  
Vol 30 (7) ◽  
pp. 1295-1315 ◽  
Author(s):  
Lúcia P. Barroso ◽  
Gauss M. Cordeiro ◽  
Klaus L. P. Vasconcellos

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