What’s New in LIMDEP 9.0?

LIMDEP 9.0 is a major expansion of our premier software for cross section, panel and time series data analysis. Version 9.0 features numerous new estimation programs, a long list of enhancements to its user interface, additions to data manipulation commands, and improvements in the internal workings of the mathematical parts of the program. The new documentation, with over 2,500 pages, contains full reference guides for the program, background econometrics, and sample applications.

New Models

Probit model with endogenous right hand side variable
Models for count data
            Polya-Aeppli and generalized Poisson models for count data
            New form of the negative binomial model for count data
            Negative binomial model with sample selection
Dynamic probit model for panel data
Numerous new forms of the stochastic frontier model
            Several forms of the Battese/Coelli model
            Stochastic frontier model with sample selection
            Data envelopment analysis for efficiency analysis
Cox model with time varying covariates
Parametric survival models with sample selection
Ordered choice models
           Generalized ordered probit
           Hierarchical ordered probit
           Zero inflated ordered probit
           Bivariate ordered probit
           Ordered probit with sample selection
Propensity score matching methods
Quantile regression model
Nested random effects model
Binomial, power, beta, Rayleigh and geometric loglinear models
Discriminant and classification analysis

Panel Data Versions of Nearly All Models

A major theme of this revision is panel data. LIMDEP now offers panel data estimators for nearly all the models that are supported by the program. These estimators represent significant innovations in the capabilities of econometric software. To our knowledge, they do not exist in any other program. The estimators are of three broad classes:

True fixed effects: You can fit fixed effects models with up to 50,000 dummy variable coefficients, to almost all of LIMDEP’s models. (In a custom extension of this method, we used LIMDEP’s algorithm to fit a probit model with over 150,000 dummy variable coefficients.) This is a true fixed effects estimator in which the dummy variable coefficients are actually computed, not swept out of the model. (In the process, we (in collaboration with Paul Allison) have remedied a major shortcoming in the longstanding negative binomial fixed effects model proposed by Hausman, Hall and Griliches in use since 1984.)

Random parameters: Full random coefficients have been proposed at several places in the literature, but almost exclusively in the setting of the linear regression model, Poisson model, and binary logit model. This version of LIMDEP extends the maximum simulated likelihood technique for random parameters models to all the models supported by LIMDEP, and several new ones that have not appeared previously, such as the sample selection model and a suite of 10 new ‘loglinear’ models.

Latent classes: Once again, this is a model that has appeared in the literature virtually exclusively in the Poisson regression model. We have extended it to over 30 different model forms, including probit, logit, tobit, truncation, Poisson, negative binomial, stochastic frontier, and so on.
Some of these have been extended to multiple equation settings as well, including forms of the bivariate probit model and sample selection models.

We have also extended the linear regression model with panel data in several directions, including the addition of the Hausman and Taylor estimator for random effects and the Arellano, Bond, and Bover GMM estimator for dynamic panel data models. We have also built a random coefficients estimator for the linear regression model which does not require every group to have more observations than there are variables in the model. The program panel data treatment of the linear model also adds a number of features which will make estimation much easier.

Extensions of Existing Models and Techniques

Random effects multinomial logit model for panel data
Multilevel and nested multilevel random effects models
Extensions of several models for latent heterogeneity and heteroscedasticity

Extensions of Estimation and Analysis Methods

Variance corrections in all models for clustered and complex survey data
Jackknife estimator for asymptotic variances
Linear constraints in general maximum likelihood estimation
Generalized maximum entropy estimation for multinomial logit models
Simulation analysis for binary choice models
Extensions of plotting and histogram programs
Export functions for model results
Many extensions of the programming tools for writing procedures and new estimators
Krinsky and Robb estimators for standard errors for nonlinear functions

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