Panel Data Models

The full range of treatments to exploit longitudinal data are supported for all models included in the 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 and 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 and 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
  • Arellano and Bonds’s GMM estimator for dynamic models
  • Hausman and Taylor’s estimator for random effects
  • Probit, logit, Gompertz, complementary log log 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, ordered Gompertz, ordered complementary log log,
  • Sample selection
  • 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