LIMDEP Version 10 is an integrated statistical package for estimation and analysis of linear and nonlinear models, with cross section, time series and panel data. LIMDEP has long been a leader in the field of econometric and statistical analysis and has provided many recent innovations including cutting edge techniques in panel data analysis, frontier and efficiency estimation and discrete choice modeling. The collection of techniques and procedures for analyzing panel data is without parallel in any other statistical software package available anywhere. Recognized for years as the standard software for the estimation and manipulation of discrete and limited dependent variable models, LIMDEP 10 is now unsurpassed in the breadth and variety of its estimation tools. The main feature of the package is a suite of more than 100 built-in estimators for all forms of the linear regression model, and stochastic frontier, discrete choice and limited dependent variable models, including models for binary, censored, truncated, survival, count, discrete and continuous variables and a variety of sample selection models. No other program offers a wider range of single and multiple equation linear and nonlinear models. LIMDEP is a true state-of-the-art program that is used for teaching and research at thousands of universities, government agencies, research institutes, businesses and industries around the world.

References in zbMATH (referenced in 77 articles )

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  1. Álvarez, Inmaculada C.; Barbero, Javier and Zofío, José L: A Data Envelopment Analysis Toolbox for MATLAB (2020) not zbMATH
  2. Balk, Bert M.; Barbero, Javier; Zofío, José L.: A toolbox for calculating and decomposing total factor productivity indices (2020)
  3. Shi, Lei: Bayesian analysis of multivariate ordered probit model with individual heterogeneity (2020)
  4. Bergtold, Jason S.; Pokharel, Krishna P.; Featherstone, Allen M.; Mo, Lijia: On the examination of the reliability of statistical software for estimating regression models with discrete dependent variables (2018)
  5. de Andrade, Bernardo B.; Souza, Geraldo S.: Likelihood computation in the normal-gamma stochastic frontier model (2018)
  6. Wyszynski, Karol; Marra, Giampiero: Sample selection models for count data in R (2018)
  7. Jonathan Holtkamp; Bernhard Brümmer: Stochastic Frontier Analysis Using SFAMB for Ox (2017) not zbMATH
  8. Mauricio Sarrias: Discrete Choice Models with Random Parameters in R: The Rchoice Package (2016) not zbMATH
  9. Shaik, Saleem: Impact of liquidity risk on variations in efficiency and productivity: a panel gamma simulated maximum likelihood estimation (2015)
  10. Zhang, Rong; Inder, Brett A.; Zhang, Xibin: Bayesian estimation of a discrete response model with double rules of sample selection (2015)
  11. Maria Karlsson, Anita Lindmark: truncSP: An R Package for Estimation of Semi-Parametric Truncated Linear Regression Models (2014) not zbMATH
  12. Adkins, Lee C.: Testing parameter significance in instrumental variables probit estimators: some simulation (2012)
  13. A. Talha Yalta, Sven Schreiber: Random Number Generation in gretl (2012) not zbMATH
  14. Chesher, Andrew; Smolinski, Konrad: IV models of ordered choice (2012)
  15. Wheat, Phill; Smith, Andrew: Is the choice of ((t-T)) in Battese and Coelli (1992) type stochastic frontier models innocuous? Observations and generalisations (2012)
  16. Simar, Léopold; Wilson, Paul W.: Inferences from cross-sectional, stochastic frontier models (2010)
  17. Yalta, A. Talha; Yalta, A. Yasemin: Should economists use open source software for doing research? (2010) ioport
  18. Hedeker, Donald; Demirtas, Hakan; Mermelstein, Robin J.: A mixed ordinal location scale model for analysis of ecological momentary assessment (EMA) data (2009)
  19. Hilbe, Joseph M.: Logistic regression models. (2009)
  20. Hwang, Ruey-Ching; Cheng, K. F.; Lee, Cheng-Few: On multi-class prediction of issuer credit ratings (2009)

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