The literature I am basing my work on is cross-sectional data (I aim for my contribution to the literature to be that I am using panel data methods instead) so unfortunately I cannot say that the literature uses FE or RE.The paper examines the impact of corporate governance (CG), capital structure (CS) on firm value (FV) of firms in Vietnam. Q2: Could I use -xtprobit, re- instead? If so, then I think that I would not need to conduct a Hauman test, as there is no -xtprobit, re. Q1: Should I let Stata run overnight to see if -xtlogit, fe- produces an outcome? Or is it not worth using -xtlogit, fe-, because I can foresee further analysis being complicated by the large amount of time it takes to run? Please could someone advise me on the next course of action? xtlogit, re- produces an outcome in a couple of minutes, but -xtlogit, fe- will take hours.
Integration method: mvaghermite Integration pts. Random effects u_i ~ Gaussian Obs per group: Group variable: hhid Number of groups = 1,722 Random-effects logistic regression Number of obs = 5,255 xtlogit saving $xlist $controllist i.year, re nolog Last estimation results not found, nothing to store
Note: male omitted because of no within-group variance. Note: 1,260 groups (2,748 obs) dropped because of all positive or Note: multiple positive outcomes within groups encountered. xtlogit saving $xlist $controllist i.year, fe nolog It also drops my -male- binary variable, which would be better kept in for my analysis.Ĭode. This concerns me, as I believe many individuals are being dropped out because they have responded yes to -saving- in all waves they participated in, or no in all waves.
When I run -xtlogit, fe-, I lose many observations. +-Ģ976 100.00 | XXXXXXXXXXXXXThe dependent variable is binary, measuring an individual's ability to save (saving=1 if individual indicated an ability to save 0 otherwise).Īs -saving- is binary, I should be using nonlinear methods (either xtprobit or xtlogit). (hhid*year uniquely identifies each observation)ĭistribution of T_i: min 5% 25% 50% 75% 95% max
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