Quantile Regression for Logarithmic Relationships with Non- Positive Outcome Values // Provides the lzrq() function for estimating logarithmic regression slopes in quantile regression models, permitting the outcome variable to take on non-positive values. lzrq() conducts regression after replacing non-positive values with a sufficiently negative value. If the fitted values of a quantile regression on this transformed outcome are all greater than the negative value, then results are displayed. The resulting coefficients can be meaningfully interpreted as logarithmic intensive-margin relationships between the outcome variable and the independent variables, even with non-positive values in the outcome variable. If the condition does not hold for the specified quantile, then the command iteratively makes the value larger and checks again. After ten iterations where the condition does not hold, the functions return an error and suppress results. This is an automated adaptation of the algorithm described by Liu Kaplan (2025) https://drive.google. com/file/d/1F3dnhm8MrlO5aRrGt48rBWAEaBqdCBH-/view and implemented in the companion 'Stata' command 'lzqreg', described in Fitzgerald et al. (2026) doi:10.31222/osf.io/juda7_v1.