Why TransformY? The Pitfalls of Transformed Regressions with a Mass at Zero
指出，对非负且右偏的因变量进行对数或反双曲正弦变换时，会将零值分离出来，导致估计参数与缩放线性概率模型相关，且反变换后的边际效应和弹性对形状参数敏感。建议改用两部分模型、未变换线性回归或泊松回归。
Abstract Applied economists often transform a dependent variable that is non‐negative and skewed with the natural log transformation, the inverse hyperbolic sine transformation, or power function. We show that these transformations separate the zeros from the positives such that the estimated parameters are related to those from a scaled linear probability model. The retransformed marginal effects and elasticities are sensitive to changes in a shape parameter, ranging in magnitude between those of an untransformed least squares regression and those of a scaled linear probability model. Instead of transforming the dependent variable with non‐negative outcomes that includes zeros, we recommend using a non‐transformed dependent variable, such as a two‐part model, untransformed linear regression, or Poisson.