lzrq: 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>.
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