  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.
