Panel Data Models
The full range of treatments to exploit longitudinal data are supported for all models included in LIMDEP and NLOGIT. No panel data operation anywhere in the program requires that the data set be balanced. Most estimators place no limit on the number of groups in the panel. The following lists program features for specific types of panel data models:
- Fixed & Random Effects Linear Models
- Nonlinear Fixed Effects Models
- Random Effects Models
- Random Parameters - Mixed Models
- Latent Class Models
Conditional Logit, Ordered Probit, Loglinear Models, Limited Dependent Variables & More
Nearly all of the models in LIMDEP and NLOGIT may be analyzed with special tools for panel data. A partial list of the panel data models supported in LIMDEP and NLOGIT includes:
- Linear regression model, OLS, GLS, 2SLS, IV, first difference
- Arellano and Bonds’s GMM estimator for dynamic models
- Hausman and Taylor’s estimator for random effects
- Probit, logit, binary choice
- Tobit, truncated and censored regression, categorical data
- Survival models: exponential, Weibull, lognormal, loglogistic
- Loglinear models: Weibull, gamma, exponential, inverse Gauss, generalized beta
- Stochastic frontier
- Bivariate probit
- Partial observability
- Ordered probit, ordered logit, bivariate ordered probit
- Sample selection in linear, probit, ordered probit, stochastic frontier, Poisson models
- Poisson, negative binomial, zero inflated Poisson, hurdle Poisson and negative binomial
- Censoring and truncation models for count variables
- Conditional logit and multinomial logit
- Conditional maximum likelihood estimates for linear regression, binomial logit, Poisson and negative binomial