Random-effects panel-data estimators

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Random-effects panel-data estimators were introduced in Stata 13.

See the latest version of random-effects panel-data estimators. See all of Stata's panel-data features.

See the new features in Stata 17.

Random-effects panel-data estimators

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Highlights

New estimators
- Random-effects ordered probit
- Random-effects ordered logit
- Random-effects multinomial logit (via generalized SEM)
Cluster–robust standard errors

Relax distributional assumptions
Allow for correlated data
Available on new estimators
Also available on probit, logit, complementary log-log, and Poisson

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It is difficult to say panel data without saying random effects. Panel data are repeated observations on individuals. Random effects are individual-level effects that are unrelated to everything else in the model.

Say we have data on 4,708 employees of a large multinational corporation. We have repeated observations on these employees over the years. On average, we have 6 years of data. For some employees, we have 15 years.

Our data include professional status (1, 2, 3, or 4), age, education, and years of job experience.

We fit the following model:

. xtset idcode year panel variable: idcode (unbalanced) time variable: year, 68 to 88, but with gaps delta: 1 unit . xtoprobit status educ age age2 experience Random-effects ordered probit regression Number of obs = 28508 Group variable: idcode Number of groups = 4708 Random effects u_i ~ Gaussian Obs per group: min = 1 avg = 6.1 max = 15 Integration method: mvaghermite Integration points = 12 Wald chi2(4) = 5676.07 Log likelihood = -26032.326 Prob > chi2 = 0.0000


status		Coef. Std. Err. z P>\|z\| [95% Conf. Interval]

educ		.333993 .0091215 36.62 0.000 .3161152 .3518707
age		.2129696 .0123513 17.24 0.000 .1887616 .2371777
age2		-.004221 .0002029 -20.80 0.000 -.0046188 -.0038232
experience		.1918314 .004661 41.16 0.000 .1826959 .2009669

/cut1		7.625209 .2156879 35.35 0.000 7.202468 8.047949
/cut2		8.498753 .217015 39.16 0.000 8.073411 8.924094
/cut3		10.07729 .219697 45.87 0.000 9.646696 10.50789

/sigma2_u		1.498732 .0506867 1.402609 1.601443

LR test vs. oprobit regression: chibar2(01) = 9467.03 Prob>=chibar2 = 0.0000

We find that the probability of the highest status level increases with education and experience. We also find that individuals have a large permanent component (/sigma2_u, the variance of the random effect, is both large and significant).