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.,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).,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.,The originality of this study lies in demonstrating an alternative microeconometric technique to deal with positive skewness of dependent variables.

中文翻译：

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估计对数转换因变量的泊松伪最大似然而不是对数线性模型

本研究的目的是解释最近使用微观数据的非主流计量经济学方法，以及它如何为工商管理研究提供信息。更具体地说，该论文借鉴了应用微观计量经济学文献的立场，赞成使用稳健的标准误差拟合泊松回归，而不是对数转换因变量的 OLS 线性回归。此外，作者指出了适当的Stata编码，并考虑了未能检查估计存在的可能性——收敛问题——以及对数值问题的敏感。作者详细说明了日志的主要问题-线性模型，从应用计量经济学文献中提取，有利于估计非计数数据的乘法模型。然后，