Improved heteroskedasticity likelihood ratio tests in symmetric nonlinear regression models

Mariana C. Araújo (), Audrey H. M. A. Cysneiros and Lourdes C. Montenegro
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Mariana C. Araújo: Universidade Federal do Rio Grande do Norte
Audrey H. M. A. Cysneiros: Universidade Federal de Pernambuco
Lourdes C. Montenegro: Universidade Federal de Minas Gerais

Statistical Papers, 2020, vol. 61, issue 1, No 9, 167-188

Abstract: Abstract In this paper we address the issue of testing inference of the dispersion parameter in heteroscedastic symmetric nonlinear regression models considering small samples. We derive Bartlett corrections to improve the likelihood ratio as well modified profile likelihood ratio tests. Our results extend some of those obtained in Cordeiro (J Stat Comput Simul 74:609–620, 2004) and Ferrari et al. (J Stat Plan Inference 124:423–437, 2004), who consider a symmetric nonlinear regression model and normal linear regression model, respectively. We also present the bootstrap and bootstrap Bartlett corrected likelihood ratio tests. Monte Carlo simulations are carried out to compare the finite sample performances of the three corrected tests and their uncorrected versions. The numerical evidence shows that the corrected modified profile likelihood ratio test, the bootstrap and bootstrap Bartlett corrected likelihood ratio test perform better than the other ones. We also present an empirical application.

Keywords: Bartlett correction; Bootstrap; Likelihood ratio test; Modified profile likelihood ratio test (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s00362-017-0933-5

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