Estimating Poisson pseudo-maximum-likelihood rather than log-linear model of a log-transformed dependent variable

Victor Motta

RAUSP Management Journal, 2019, vol. 54, issue 4, 508-518

Abstract: Purpose - The purpose of this study is to account for a recent non-mainstream econometric approach using microdata and how it can inform research in business administration. More specifically, the paper draws from the applied microeconometric literature stances in favor of fitting Poisson regression with robust standard errors rather than the OLS linear regression of a log-transformed dependent variable. In addition, the authors point to the appropriate Stata coding and take into account the possibility of failing to check for the existence of the estimates – convergency issues – as well as being sensitive to numerical problems. Design/methodology/approach - The author details the main issues with the log-linear model, drawing from the applied econometric literature in favor of estimating multiplicative models for non-count data. Then, he provides the Stata commands and illustrates the differences in the coefficient and standard errors between both OLS and Poisson models using the health expenditure dataset from the RAND Health Insurance Experiment (RHIE). Findings - The results indicate that the use of Poisson pseudo maximum likelihood estimators yield better results that the log-linear model, as well as other alternative models, such as Tobit and two-part models. Originality/value - The originality of this study lies in demonstrating an alternative microeconometric technique to deal with positive skewness of dependent variables.

Keywords: Health economics; Applied microeconometrics; Poisson pseudo maximum likelihood estimator (search for similar items in EconPapers)
Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
https://www.emerald.com/insight/content/doi/10.110 ... d&utm_campaign=repec (text/html)
https://www.emerald.com/insight/content/doi/10.110 ... d&utm_campaign=repec (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eme:rauspp:rausp-05-2019-0110

DOI: 10.1108/RAUSP-05-2019-0110

Access Statistics for this article

RAUSP Management Journal is currently edited by Flavio Hourneaux Junior and Kavita Miadaira Hamza

More articles in RAUSP Management Journal from Emerald Group Publishing Limited
Bibliographic data for series maintained by Emerald Support ().