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LIMDEP and its addon NLOGIT provides all that is needed for any kind of statistical analysis in the field of econometrics. Standard models like ARIMAX and linear regression are available but LIMDEP provides even more sophisticated features like generalized linear regression (for binary, multinomial and count-data responses) as well. A special strength of LIMDEP and NLOGIT is the class of discrete choice models including features like random effects and panel-data-analysis.
NLOGIT offers even more specialized methods - especially in the context of discrete choice models. NLOGIT extends the class of discrete choice models to generalized mixed Logit- and generalized nested Logit-models. Additionally NLOGIT provides a powerful simulation-suite, helping you to generate forecasts for many different scenarios.
Finally the internal scripting language provides all the flexibility you will ever need in a statistics package.
Benefits from NLOGIT and LIMDEP:
- All relevant methods for econometric research
- Supports discrete choice models to model brand preferences
- Flexible and powerful scripting language
- Simulation-suite for scenario-based forecasts
- Even more specialized methods provided by NLOGIT
NLOGIT (incl. LIMDEP)
NLOGIT is the world's leading package for analysis and simulation of discrete choice data such as brand choice, transportation mode, and all manner of survey and market data.
NLOGIT includes all of LIMDEP plus NLOGIT's FIML estimation programs.
NLOGIT provides estimation programs for all up to date techniques including mixed (random parameters) logit, nested logit, multinomial probit, and heteroscedastic extreme value. Complete flexibility in variable choice set specifications is supported. Data may be individual observations, rankings, frequencies or market shares, and data sets may combine revealed and stated preference data.
NLOGIT is an extension of LIMDEP that, in addition to all features of LIMDEP, provides programs for estimation, model simulation and analysis of multinomial choice data, such as brand choice, transportation mode, and all manner of survey and market data in which consumers choose among a set of competing alternatives. is a full information maximum likelihood estimator for, among other models, up to four level nested logit models. Many other formulations are included in NLOGIT, including random parameters (mixed logit), latent class, multinomial probit, many forms of the nested logit model, and several new formulations for panel data. NLOGIT is a superset of LIMDEP and includes all the features and capabilities of LIMDEP plus NLOGIT's FIML estimation programs. With the combination of LIMDEP and NLOGIT, NLOGIT is the only large package for discrete choice analysis that contains the full set of features of an integrated econometrics program.
LIMDEP and its extension NLOGIT – a complete Econometrics Package
NLOGIT offers a complete set of tools for econometric analysis. In addition to the estimation programs, NLOGIT provide:
- Data management, including input from all standard sources (such as Excel), all manner of transformations and sample controls
- Built-in estimation programs plus a programming language, matrix algebra package and scientific calculator that allow you to write your own estimators, test statistics and simulation and analysis programs
- Random number, vector and matrix capabilities for bootstrapping, Gibbs sampling and Monte Carlo simulation
- A wide range of graphical and numeric descriptive statistics capabilities
- Optimization tools that allow you to construct your own likelihood, GMM, or maximum simulated likelihood estimators
- Analysis tools including graphics, numerical analysis and post estimation tools for specification and hypothesis testing
- Easily searchable completely reworked electronic Reference Guide with extensive explanatory text and dozens of new examples included
|Further Requirements||DVD-ROM drive|
|Operating System||Windows XP, Vista, 7 (32-/64-Bit), Windows 10|
|Min. RAM||512 MB|
|Disk Space||Minimum of 100 MB of disk space|
What’s New in NLOGIT Version 6 Software?
All the new features described for LIMDEP 11 are in NLOGIT 6. In addition, there are many new features in Version 6. We have added several enhancements to give you greater flexibility in analyzing different types of data. Many of the features of NLOGIT, existing and new, are designed to let you go beyond just computing coefficients, to analyzing and using your model. We have added many new models including the random regret logit model and best/worst outcome. NLOGIT 6 continues to pioneer new developments for estimation in WTP (willingness to pay) space. Altogether, we have added dozens of features in NLOGIT 6, some clearly visible ones such as the new models and some ‘behind the scenes’ that will smooth the operation and help to stabilize the estimation programs. The following will summarize the important new developments.
New Multinomial Choice Models
NLOGIT 6 includes many new commands and extension of the random parameters model and latent class models:
- Fixed effects in multinomial logit models
- Random effects multinomial logit models
- Random regret logit model
- Best/worst outcome data
- Berry, Levinsohn and Pakes random parameters logit model
- Latent classes with random parameters
- Generalized mixed logit
- Willingness to pay
- Attribute nonattendance (explicit and implicit)
- Individual specific expected parameters
- Model simulation
- Estimated elasticities and partial effects
- Robust covariance matrix
- Random data generators
- Posterior estimates from latent class models
- Coefficients in random parameters models
- Simplified WALD command
More NLOGIT Features
NLOGIT includes all of LIMDEP plus the full set of features in NLOGIT, including the additional data management features, estimators for many types of discrete choice models, and the program simulator.
- Data Analysis
NLOGIT will typically be used to analyze individual, cross section data on consumer choices and decisions from multiple alternatives. But, the program is equally equipped for market shares or frequency data, data on rankings of alternatives, and, for several of the estimators, panel data from repeated observation of choice situations. There are several data handling procedures for NLOGIT in addition to all those available in LIMDEP.
- Model Estimation
NLOGIT supports a greater range of models for discrete choice than any other package. These include state of the art estimators for the mixed (random parameters) logit model, WTP space, random regret, and nonlinear utility models. The basic multinomial logit model, nested logit models up to four levels, the multinomial probit model are also supported.
NLOGIT contains all of the discrete choice estimators supported by LIMDEP, plus the extensions of the discrete choice models which do not appear in LIMDEP.
- Multinomial logit - many specifications
- Random effects MNL
- Generalized mixed logit
- Random regret logit
- MNL with nonlinear utility functions
- WTP space specifications in mixed logit
- Scaled multinomial logit
- Nested logit
- Generalized nested logit
- Multinomial probit
- Mixed (random parameters) logit
- Heteroscedastic extreme value
- Covariance heterogeneity
- Latent class
- Latent class random parameters
- Nonlinear utilities with random parameters
- Model Specification
NLOGIT’s estimation programs are accessed as LIMDEP model commands. Since discrete choice models are often more complicated to specify than other single equation models in LIMDEP, the command setup includes many specifications that are specific to NLOGIT.
- Inference Tools for Hypothesis Testing
The full set of post estimation and analysis tools in LIMDEP is accessed by NLOGIT. This includes the Wald, likelihood ratio and Lagrange multiplier tests, and all the matrix algebra and scientific calculator tools. NLOGIT also provides tools specific for discrete choice analysis, including built-in procedures for testing the IIA assumption of the multinomial logit model.
Any model estimated by NLOGIT can be used in ‘what if’ analyses using the model simulation package. The base case model produces fitted probabilities data that aggregate to a prediction of the sample shares for the alternatives in the choice set. The simulator is then used, with the estimation data set or any other compatible data set, to recompute these shares under scenarios that you specify, such as a change in the price of a particular alternative or a change in household incomes.