Estimating grouped data models with a binary dependent variable and fixed effect via logit vs OLS: the impact of dropped units

View Researcher's Other Codes

Disclaimer: The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).

Authors Nathaniel Beck
Journal/Conference Name ARXIV: APPLICATIONS
Paper Category
Paper Abstract This letter deals with a very simple issue: if we have grouped data with a binary dependent variable and want to include fixed effects (group specific intercepts) in the specification, is Ordinary Least Squares (OLS) in any way superior to a logit form because the OLS method \emph{appears} to keep all observations whereas the logit drops all groups which have either all zeros or all ones on the dependent variable? It is shown that OLS averages the estimates for the all zero (and all one) groups, which by definition have all slope coefficients of zero, with the slope coefficients for the groups with a mix of zeros and ones. Thus the correct comparison of OLS to logit is to only look at groups with some variation in the dependent variable. Researchers using OLS are urged to report results both for all groups and for the subset of groups where the dependent variable varies. The interpretation of the difference between these two results depends upon assumptions which cannot be empirically assessed.
Date of publication 2018
Code Programming Language R

Copyright Researcher 2022