Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/31110 
Year of Publication: 
2006
Series/Report no.: 
Discussion Paper No. 474
Publisher: 
Ludwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen, München
Abstract: 
Count data often exhibit overdispersion and/or require an adjustment for zero outcomes with respect to a Poisson model. Zero-modified Poisson (ZMP) and zeromodified generalized Poisson (ZMGP) regression models are useful classes of models for such data. In the literature so far only score tests are used for testing the necessity of this adjustment. For this testing problem we show how poor the performance of the corresponding score test can be in comparison to the performance of Wald and likelihood ratio (LR) tests through a simulation study. In particular, the score test in the ZMP case results in a power loss of 47% compared to the Wald test in the worst case, while in the ZMGP case the worst loss is 87%. Therefore, regardless of the computational advantage of score tests, the loss in power compared to the Wald and LR tests should not be neglected and these much more powerful alternatives should be used instead. We also prove consistency and asymptotic normality of the maximum likelihood estimators in the above mentioned regression models to give a theoretical justification for Wald and likelihood ratio tests.
Subjects: 
generalized Poisson distribution
likelihood ratio test
maximum likelihood Estimator
overdispersion
score test
Wald test
zero-modification
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

Files in This Item:
File
Size
204.43 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.