Order by request
Ab einer bestimmten Bestellmenge stellen wir Ihnen ein individuelles Angebot zusammen.
Dieser Artikel wird Ihrem Warenkorb beigelegt, wird jedoch nicht in der Berechnung berücksichtigt
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 5.0 includes all of LIMDEP 10 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 Version 5 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.
NLOGIT has become the premier package for estimation and simulation of multinomial discrete choice models. Version 5.0 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 5 is a superset of LIMDEP and includes all the features and capabilities of LIMDEP 10 plus NLOGIT's FIML estimation programs. With the combination of LIMDEP and NLOGIT, NLOGIT 5 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)|
|Min. RAM||512 MB|
|Disk Space||Minimum of 100 MB of disk space|
LIMDEP 10 Features
All the new features described for LIMDEP 10 are in NLOGIT 5. In addition, there are many new features in Version 5.
New Multinomial Choice Models
- Generalized Mixed Logit Model – The generalized mixed logit (GMXL) model accommodates both random parameters in the utility functions and randomly variable scaling of the entire preference structure. The GMXL model is at the frontier of mixed multinomial choice modeling and NLOGIT provides many different variations on the model.
- Nonlinear Utilities in the Mixed Logit Model – NLOGIT 5’s random parameters (mixed) logit model is extended to allow nonlinear utility functions. This capability vastly generalizes the model – utility functions may be any nonlinear or linear function that can be specified using the program syntax for nonlinear models.
- Innovations in Multinomial Choices
New model frameworks include several innovations.
- Latent class model with random parameters in each class
- Scaled multinomial logit
- Random regret MNL – this model explores an alternative to utility maximization as the model basis
- Attribute nonattendance – this model accommodates the latent possibility that some respondents do not attend to all attributes in making their choices
- Estimation in ‘willingness to pay space.’ This approach to model estimation works around the problem of using ratios of estimates to estimate willingness to pay. The ratios can behave erratically when they are close to zero. Estimation in WTP space, via a nonlinear transformation of the model, circumvents the problem by making the WTP coefficient the structural parameters in the model.
- Utility Scaling – Heterogeneity in preference structures may take the form of general scaling of the entire utility framework. NLOGIT 5 provides general scaling in the multinomial logit model, the latent class model and the generalized mixed model. All of these can be layered into models based on stated choice data.
- Mixed Logit Models – The mixed logit model represents the frontier in multinomial choice modeling. We have added many new features to NLOGIT’s already major implementation of this model. A partial list includes:
- The latent class model may now have random parameters in each class.
- The generalized mixed logit model allows random parameters and random scaling of the entire preference structure.
- Heterogeneity in random parameters and generalized mixed logit models may appear in the variances as well as the means
- Partial Effects and Elasticities – Elasticities have been reformatted so that tables may be exported to spreadsheet programs such as Excel. The results in the figure we xported directly to Excel. Elasticities may also be formatted as matrices to analyze within NLOGIT or export to other programs.
Competely reworked NLOGIT 5 Reference Guide
- Portabel and easily searchable new electronic format
- Included: Documentation of the foundational discrete choice models described in detail in the LIMDEP Econometric Modeling Guide, including binary choice and ordered choice models.
- Included: Extensive explanatory text and dozens of new examples, with applications for every technique and model presented.
- Model Estimation and Analysis
Over 100 model formulations for continuous, discrete, limited and censored dependent variables are provided, including:
- Linear and nonlinear regression
- Robust estimation
- Binary choice
- Ordered choice models
- Unordered multinomial choice
- Censoring and truncation
- Sample selection models
- Count data
- Loglinear models
- Stochastic frontier and DEA
- Survival analysis
- Quantile regression (linear and count)
- Time series models
- Panel data models
- Analysis of Model Results
Programming language allows extensions of supported estimators:
- Nonlinear estimation
- Delta method for functions of parameters
- Simulation: Krinsky and Robb
- Testing and restrictions
- Post estimation analysis
- Partial effects for all models
- Oaxaca decomposition
- Panel Data Models
All of the linear and nonlinear models may be analyzed with special forms of panel data, including:
- Fixed and random effects
- Multilevel random effects
- Latent class models
- Random parameters (mixed) models
- Unbalanced panels for all models
- Unlimited panel data set size
- Arellano/Bond DPD with many variations
- IV and GMM estimators
- Data Description and Graphics
Descriptive statistics and graphical analysis tools include:
- Descriptive statistics for cross sections and panels
- Tables of means and quantiles
- Time series
- Spectral density
- Graphics tools
- Kernel density
- Discriminant analysis
- Contour plots
- Count Data
The widest range of specifications for count data of any package is provided, including several newly developed models:
- Poisson and negative binomial models
- New specifications for NB models
- Gamma, generalized Poisson, Polya-Aeppli
- Zero inflation and hurdle
- Fixed and random effects
- Latent class
- Quantile Poisson regression
- Data Environments
Nearly every model may be extended to a variety of frameworks including:
- Data transformations
- Multiple imputation
- Cross section
- Panel data
- Time series manipulation
- Programming and Numerical Analysis
Programming language including matrix and data manipulation commands is provided for building new estimators:
- MAXIMIZE/MINIMIZE for user supplied functions
- Matrix programming with LIMDEP
- Scientific calculator
- Numerical analysis tools, integration and differentiation
- Simulation based estimation
- Program Gibbs samplers
- Frontier and Efficiency Analysis
All forms of the stochastic frontier model are provided:
- Fixed and random effects
- True fixed and random effects
- Latent class stochastic frontier
- Battese and Coelli
- Technical inefficiency estimation
- Data envelopment analysis (This is the only package with both SFA and DEA.)
- Discrete Choice Models in LIMDEP
Discrete choice estimators for binary, multinomial, ordered, count and multivariate discrete data are provided:
- Binary choice - dozens of specifications
- Ordered choice
- Hierarchical ordered choice
- Panel data
- Multinomial logit
- Count data models
- Bivariate binary and ordered choice
- Discrete choice with sample selection
- Time Series Analysis
A range of estimators for time series are provided including:
- ARMAX models
- GARCH and GARCH-in-mean models
- Spectral density estimation
- ACF and PACF
- Phillips-Perron tests
- Newey-West estimator
Extremely accurate computational methods are employed throughout. High marks are earned on all National Institute of Standards and Technology test problems, including:
- Descriptive statistics
- Analysis of variance
- Linear regression
- Nonlinear least squares
- Post Estimation
Extensive tools for post estimation enable manipulation of model results along with other statistics and procedures.
- Data Management
Data management tools are provided for input of data or internal generation with the random number generators, including:
- Data transformations
- Sampling and bootstrapping
- Bootstrap cross section observations or panel groups
- Weighted data
- Random number generation
- Cluster sampling and stratification
- Multiple Imputation
Multiple Imputation is used to generate proxies for missing values in order to use information from the model and within the sample to increase the precision of estimators. Missing values for continuous, binary, count, Likert, fractional and multinomial data may be generated. Results from multiple samples are generated and averaged to produce the final results.
NLOGIT 5 Features
NLOGIT 5 includes all of LIMDEP 10 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.