Testing and correcting for distributional misspecifications in the Tobit model: An application of the Information Matrix test

1991 ◽  
Vol 16 (3) ◽  
pp. 313-323 ◽  
Author(s):  
A. Reynolds ◽  
J. S. Shonkwiler
Keyword(s):  
Information Matrix ◽  
Tobit Model ◽  
Information Matrix Test

Assessing Person Fit With the Information Matrix Test

Methodology ◽  
2015 ◽  
Vol 11 (1) ◽  
pp. 3-12 ◽  
Author(s):  
Jochen Ranger ◽  
Jörg-Tobias Kuhn
Keyword(s):  
Simulation Study ◽  
Type I Error ◽  
Information Matrix ◽  
Small Samples ◽  
Type I ◽  
Person Fit ◽  
Power Of The Test ◽  
Order Expansion ◽  
Trait Stability ◽  
Information Matrix Test

In this manuscript, a new approach to the analysis of person fit is presented that is based on the information matrix test of White (1982) . This test can be interpreted as a test of trait stability during the measurement situation. The test follows approximately a χ2-distribution. In small samples, the approximation can be improved by a higher-order expansion. The performance of the test is explored in a simulation study. This simulation study suggests that the test adheres to the nominal Type-I error rate well, although it tends to be conservative in very short scales. The power of the test is compared to the power of four alternative tests of person fit. This comparison corroborates that the power of the information matrix test is similar to the power of the alternative tests. Advantages and areas of application of the information matrix test are discussed.

scholarly journals Testing for Consistency using Artificial Regressions

1989 ◽  
Vol 5 (3) ◽  
pp. 363-384 ◽  
Author(s):  
Russell Davidson ◽  
James G. MacKinnon
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Information Matrix ◽  
Binary Choice ◽  
Parameter Estimates ◽  
Linear Regression Models ◽  
Score Tests ◽  
Binary Choice Models ◽  
Lagrange Multiplier Tests ◽  
Information Matrix Test

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.

Asymptotic Expansions of the Information Matrix Test Statistic

Econometrica ◽  
1991 ◽  
Vol 59 (3) ◽  
pp. 787 ◽  
Author(s):  
Andrew Chesher ◽  
Richard Spady
Keyword(s):  
Asymptotic Expansions ◽  
Information Matrix ◽  
Test Statistic ◽  
Information Matrix Test
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