Artikel
Generalized information matrix tests for detecting model misspecification
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.
- Sprache
-
Englisch
- Erschienen in
-
Journal: Econometrics ; ISSN: 2225-1146 ; Volume: 4 ; Year: 2016 ; Issue: 4 ; Pages: 1-24 ; Basel: MDPI
- Klassifikation
-
Wirtschaft
Hypothesis Testing: General
Estimation: General
Statistical Simulation Methods: General
Methodological Issues: General
Model Evaluation, Validation, and Selection
- Thema
-
asymptotic theory
Information Matrix Test
specification analysis
logistic regression
simulation study
information ratio
misspecification
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Golden, Richard M.
Henley, Steven S.
White, Halbert
Kashner, T. Michael
- Ereignis
-
Veröffentlichung
- (wer)
-
MDPI
- (wo)
-
Basel
- (wann)
-
2016
- DOI
-
doi:10.3390/econometrics4040046
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:41 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Golden, Richard M.
- Henley, Steven S.
- White, Halbert
- Kashner, T. Michael
- MDPI
Entstanden
- 2016
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