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Econometrics 2016, 4(4), 46; doi:10.3390/econometrics4040046

Generalized Information Matrix Tests for Detecting Model Misspecification

1
School of Behavioral and Brain Sciences, GR4.1, 800 W. Campbell Rd., University of Texas at Dallas, Richardson, TX 75080, USA
2
Martingale Research Corporation, 101 E. Park Blvd., Suite 600, Plano, TX 75074, USA
3
Department of Medicine, Loma Linda University School of Medicine, Loma Linda, CA 92357, USA
4
Department of Economics, University of California San Diego, La Jolla, CA 92093, USA
5
Office of Academic Affiliations (10A2D), Department of Veterans Affairs, 810 Vermont Ave. NW (10A2D), Washington, DC 20420, USA
6
Center for Advanced Statistics in Education, VA Loma Linda Healthcare System, Loma Linda, CA 92357, USA
7
Department of Psychiatry, University of Texas Southwestern Medical Center at Dallas, Dallas, TX 75390, USA
Halbert White sadly passed away before this article was published.
*
Author to whom correspondence should be addressed.
Academic Editors: Kerry Patterson and Marc S. Paolella
Received: 29 December 2015 / Revised: 13 September 2016 / Accepted: 26 October 2016 / Published: 15 November 2016
(This article belongs to the Special Issue Recent Developments of Specification Testing)
View Full-Text   |   Download PDF [1052 KB, uploaded 15 November 2016]   |  

Abstract

Generalized Information Matrix Tests (GIMTs) have recently been used for detecting the presence of misspecification in regression models in both randomized controlled trials and observational studies. In this paper, a unified GIMT framework is developed for the purpose of identifying, classifying, and deriving novel model misspecification tests for finite-dimensional smooth probability models. These GIMTs include previously published as well as newly developed information matrix tests. To illustrate the application of the GIMT framework, we derived and assessed the performance of new GIMTs for binary logistic regression. Although all GIMTs exhibited good level and power performance for the larger sample sizes, GIMT statistics with fewer degrees of freedom and derived using log-likelihood third derivatives exhibited improved level and power performance. View Full-Text
Keywords: asymptotic theory; Information Matrix Test; specification analysis; logistic regression; simulation study; information ratio; misspecification asymptotic theory; Information Matrix Test; specification analysis; logistic regression; simulation study; information ratio; misspecification
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MDPI and ACS Style

Golden, R.M.; Henley, S.S.; White, H.; Kashner, T.M. Generalized Information Matrix Tests for Detecting Model Misspecification. Econometrics 2016, 4, 46.

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