LIMDEP Version 8.0 Key Features

About LIMDEP
LIMDEP has long been a leader in the field of econometric analysis. Recognized for years as the standard software for the estimation and manipulation of discrete choice and limited dependent variable models, LIMDEP 8.0 is unsurpassed in the breadth and variety of its estimation tools for cross section, panel and time series data. No other program offers a wider range of regression, panel data, survival, frontier, discrete and count data, limited dependent variable and single and multiple equation linear and nonlinear models - over 100 fully automated estimation models. LIMDEP is a true state-of-the-art program that is now used for teaching and research at thousands of sites in universities, government and private research institutions in the U.S. and throughout the world.

LIMDEP takes the form of an econometrics studio. Analysis of a data set is done interactively in a set of windows. Program control may be from a “script” or in an unstructured session of instructions and manipulations. The program is designed to allow easy setup of data for estimation, specification of different forms of the models, experimentation with different specifications, hypothesis testing, analysis of data and model results and construction of special procedures and estimators.

Program Capabilities in Detail
LIMDEP contains extensive sets of tools for every step in the analysis of a data set. The descriptions below provide overviews of the LIMDEP program functions. Additional more detailed lists of features are given on the indicated ‘Sections’ in the lists in the Data Management and Model Estimation Sections.

Data Management
Data Management tools provide for input of data or internal generation with the random number generators, and other preparation of data for use in model estimation and analysis. Also sample definition by selection of observations or bootstrapping random samples from within the data.

  • Data Input and Output: Section 1
  • Transformations: Section 2
  • Sampling and Bootstrapping: Section 3
  • Monte Carlo Analysis: Section 4
  • Weighted Data: Section 5
  • Random Number Generation: Section 6

Tools
LIMDEP provides a complete set of analysis tools in addition to the model estimation programs.

  • Sample Definition: Include or reject by algebraic criteria, time periods by date, bootstrapping cross sections or panel data, random sampling with random number generators.
  • Matrix Algebra : Full algebraic capabilities and over 75 matrix functions such as determinant, rank, characteristic roots, several types of inverses, and so on. Data and model results are combined seamlessly in this routine.
  • Scientific Calculator: Over 100 functions plus algebraic results.
  • Estimation Programs: Nearly 100 different preprogrammed estimators for all types of cross section, time series, and panel data settings, including continuous data, discrete choice, count data, survival data, and censored, truncated and limited dependent variables
  • User Defined Models: MAXIMIZE/MINIMZE allow you to define your own optimization problem, including maximum likelihood, GMM and nonlinear least squares. These also allow quadrature and simulation estimators.
  • Programming Tools: Procedures with adjustable parameters, DO FOR, DO WHILE and DO UNTIL, EXECUTE with bootstrapping and so on allow you to write your own iterative procedures
  • Numerical Analysis: Integration, differentiation, function plotting, computation of variances for nonlinear functions of estimates.

Panel Data
Nearly all of the models in LIMDEP may be analyzed with special tools for panel data. This includes fixed and random effects, random parameters and latent class models for almost all nonlinear models supported by the package. There are also numerous special estimators for the linear model, such as Arellano and Bond’s GMM estimator for dynamic panels and Hausman and Taylor’s estimator for random effects models. 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 data set is already “in the program” so it must already fit in memory. Many tools in addition to the estimation programs are also provided. For example, you can bootstrap sample groups in your panel data set, a feature we have not seen anywhere else.

  • Fixed Effects (true fixed effects – the dummy variable coefficients are
    estimated)
  • Time series – cross section, variance structure models
  • Random effects
  • Random parameters
  • Latent class
  • Dynamic panel data models - Arelland/Bond/Bover estimator
  • Hausman and Taylor’s instrumental variable estimator

Model Estimation, Data Description and Program Features
Well over 100 model formulations continuous discrete, limited and censored dependent variables are provided. All model frameworks provide a wide variety of different variants. For example, there are well over 20 different types of models for count data.

  • Descriptive statistics for cross sections and panels: Section 7
  • Descriptive statistics for time series: Section 8
  • Graphics tools: Section 9
  • Linear Regression Models: Section 10
  • Robust Estimation, Semi- and Nonparametric: Section 11
  • Nonlinear Regression Models: Section 12
  • Binary Choice Models Multivariate Binary Choice: Section 13
  • Ordered Choice Models: Section 14
  • Multinomial Choice Models Discrete Choice: Section 15
  • Censoring and Truncation: Section 16
  • Sample Selection Models: Section 17
  • Models for Counts: Section 18
  • Stochastic Frontiers: Section 19
  • Survival Models: Section 20
  • Time series models: Section 21
  • Panel data models: Section 22
  • Model estimation: Section 23
  • Testing and Restrictions: Section 24
  • Post estimation and analysis: Section 25
  • Prediction: Section 26
  • Marginal effects: Section 27
  • The delta method: Section 28
  • Dynamic linear models: Section 29
  • Conditional fixed effects estimators: Section 30
  • Fixed effects estimators: Section 31
  • Random effects models: Section 32
  • Random parameters models and multilevel modeling: Section 33
  • Latent class models: Section 34
  • Programming with LIMDEP: Section 35
  • User defined optimization: Section 36
  • Matrix algebra: Section 37
  • Scientific calculator: Section 38
  • User written programs and estimators: Section 39
  • Numerical analysis tools: Section 40

more details?