Which commands are used for dynamic panel logit/probit model estimation in Stata?
xtprobit — Random-effects and population-averaged probit models
Description
Quick start
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Syntax
Options for RE model
Options for PA model
Remarks and examples
Stored results
Methods and formulas
References
Also see
Description
xtprobit fits random-effects and population-averaged probit models for a binary dependent
variable. The probability of a positive outcome is assumed to be determined by the standard normal
cumulative distribution function.
Quick start
Random-effects probit model of y as a function of x1, x2, and indicators for levels of categorical
variable a using xtset data
xtprobit y x1 x2 i.a
Population-averaged model with robust standard errors
xtprobit y x1 x2 i.a, pa vce(robust)
As above, but specify an autoregressive correlation structure of order 1
xtprobit y x1 x2 i.a, pa vce(robust) corr(ar 1)
Random-effects model with cluster–robust standard errors for panels nested within cvar
xtprobit y x1 x2 i.a, vce(cluster cvar)
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Statistics > Longitudinal/panel data > Binary outcomes > Probit regression (RE, PA)
Dynamic panel logit/probit models are used to estimate the probability of an event occurring over time. These models are used when the dependent variable is binary and the independent variables are continuous or categorical.
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