Testing Inference in Inflated Beta Regressions under Model Misspecification

We consider testing inference in inflated beta regressions subject to model misspecification. In particular, quasi-z tests based on sandwich covariance matrix estimators are described and their finite sample behavior is investigated via Monte Carlo simulations. The numerical evidence shows that quas...

Full description

Saved in:
Bibliographic Details
Published inCommunications in statistics. Simulation and computation Vol. 45; no. 2; pp. 625 - 642
Main Authors Souza, Tatiene C., Pereira, Tarciana L., Cribari-Neto, Francisco, Lima, Verônica M. C.
Format Journal Article
LanguageEnglish
Published Philadelphia Taylor & Francis 07.02.2016
Taylor & Francis Ltd
Subjects
Beta
Bootstrap
Computer simulation
Confidence interval
Confidence intervals
Economic models
Estimators
Inference
Inflated beta regression
Mathematical analysis
Mathematical models
Model misspecification
Monte Carlo methods
Primary 62J02; Secondary 62J20
Quasi-z test
Regression analysis
Samples
Sandwich estimator
Statistical analysis
Variable precision
z test
Online AccessGet full text
ISSN0361-0918
1532-4141
DOI10.1080/03610918.2013.867995

Cover

More Information
Summary:We consider testing inference in inflated beta regressions subject to model misspecification. In particular, quasi-z tests based on sandwich covariance matrix estimators are described and their finite sample behavior is investigated via Monte Carlo simulations. The numerical evidence shows that quasi-z testing inference can be considerably more accurate than inference made through the usual z tests, especially when there is model misspecification. Interval estimation is also considered. We also present an empirical application that uses real (not simulated) data.
Bibliography:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ObjectType-Article-1
ObjectType-Feature-2
content type line 23
ISSN:0361-0918
1532-4141
DOI:10.1080/03610918.2013.867995