LIMDEP

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 43 articles )

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  1. Wyszynski, Karol; Marra, Giampiero: Sample selection models for count data in R (2018)
  2. Jonathan Holtkamp; Bernhard Brümmer: Stochastic Frontier Analysis Using SFAMB for Ox (2017)
  3. Mauricio Sarrias: Discrete Choice Models with Random Parameters in R: The Rchoice Package (2016)
  4. Simar, Léopold; Wilson, Paul W.: Inferences from cross-sectional, stochastic frontier models (2010)
  5. Hedeker, Donald; Demirtas, Hakan; Mermelstein, Robin J.: A mixed ordinal location scale model for analysis of ecological momentary assessment (EMA) data (2009)
  6. Hilbe, Joseph M.: Logistic regression models. (2009)
  7. Hwang, Ruey-Ching; Cheng, K. F.; Lee, Cheng-Few: On multi-class prediction of issuer credit ratings (2009)
  8. McDonald, John: Using least squares and Tobit in second stage DEA efficiency analyses (2009)
  9. Grün, Bettina; Leisch, Friedrich: Identifiability of finite mixtures of multinomial logit models with varying and fixed effects (2008)
  10. McKenzie, C. R.; Takaoka, Sumiko: Underwriter reputation and switching (2008)
  11. Ott Toomet; Arne Henningsen: Sample Selection Models in R: Package sampleSelection (2008)
  12. Kalouptsidis, N.; Koutroumbas, K.; Psaraki, V.: Classification methods for random utility models with i.i.d. disturbances under the most probable alternative rule (2007)
  13. Mainardi, Stefano: Unequal access to public healthcare facilities: theory and measurement revisited (2007)
  14. Taylor, Larry W.: Nonparametric estimation of duration dependence in militarized interstate disputes (2007)
  15. Miranda-Moreno, Luis F.; Fu, Liping: A comparative study of alternative model structures and criteria for ranking locations for safety improvements (2006)
  16. Ooms, Marius; Doomik, Jurgen A.: Econometric software development: past, present and future (2006)
  17. Dimara, Efthalia; Pantzios, Christos J.; Skuras, Dimitris; Tsekouras, Kostas: The impacts of regulated notions of quality on farm efficiency: A DEA application (2005)
  18. Herrero, Ines: Different approaches to efficiency analysis. An application to the Spanish Trawl fleet operating in Moroccan waters (2005)
  19. Baltas, George: A model for multiple brand choice. (2004)
  20. Lundborg, Petter; Lindgren, Björn: Do they know what they are doing? Risk perceptions and smoking behaviour among Swedish teenagers (2004)

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