论文标题
在回归模型中处理日志和零
Dealing with Logs and Zeros in Regression Models
论文作者
论文摘要
对数线性模型在实证研究中很普遍。但是，如何处理因变量中的零仍然是一个未解决的问题。本文通过开发一个称为迭代的普通最小二乘（IOL）的新估计量来阐明并解决了零的日志。这个家族筑巢的标准方法，例如日志线性和泊松回归，提供了几种计算优势，并且对应于执行流行的$ \ log（y+1）$转换的正确方法。我们将其扩展到内源性回归器设置（I2SL），并克服了泊松模型的其他常见问题，例如控制许多固定效应。我们还开发了规范测试，以帮助研究人员在替代估计器之间进行选择。最后，通过数值模拟和地标出版物的复制来说明我们的方法。
Log-linear models are prevalent in empirical research. Yet, how to handle zeros in the dependent variable remains an unsettled issue. This article clarifies it and addresses the log of zero by developing a new family of estimators called iterated Ordinary Least Squares (iOLS). This family nests standard approaches such as log-linear and Poisson regressions, offers several computational advantages, and corresponds to the correct way to perform the popular $\log(Y+1)$ transformation. We extend it to the endogenous regressor setting (i2SLS) and overcome other common issues with Poisson models, such as controlling for many fixed-effects. We also develop specification tests to help researchers select between alternative estimators. Finally, our methods are illustrated through numerical simulations and replications of landmark publications.
