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NLOGIT 6 (inkl. Limdep 11) kaufen
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LIMDEP und seine Erweiterung NLOGIT liefern Ihnen alles, was Sie sich im Bereich der Ökonometrie wünschen. Angefangen bei einfachen Zeitreihenmodellen (ARIMAX) über klassische lineare Regression - auch für binäre, multinomiale Zielgrößen und Zähldaten - bis hin zu hochspezialisierten Modellen (Discrete Choice Models, Gemischte Modelle für Panel-Daten) deckt LIMDEP alles ab.

NLOGIT bietet als zusätzliche Funktionen noch speziellere Methoden an - besonders im Kontext der Discrete Choice Modelle (Generalized Mixed Logit, Generalized Nested Logit, ...). Ferner gibt NLOGIT Ihnen Zugriff auf die Simulations-Plattform und somit die Möglichkeit, verschiedene Vorhersagen basierend auf Ihren Modellen für unterschiedliche Szenarien zu erstellen.

Nicht zuletzt gibt die systemeigene Programmiersprache einen hohen Grad an Flexibilität.

Argumente für NLOGIT und LIMDEP:

  • Umfangreiche Funktionen im Bereich der Ökonometrie
  • Spezielle Modelle zur Modellierung von Markenpräferenzen
  • Flexible systemeigene Programmiersprache
  • Erstellt verschiedene Vorhersagen für unterschiedliche Szenarien
  • Bietet zusätzliche Funktionen für speziellere Methoden als Ergänzung zu Limdep

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NLOGIT (inkl. LIMDEP)

NLOGIT is das weltweit führende Programm zur Analyse und Simulation von diskreten Daten, wie zum Beispiel Markenpräferenz, Transportmethode sowie alle möglichen Survey und Marktdaten.

NLOGIT beinhaltet alle Funktionen der Software LIMDEP 10 plus die FIML-Schätzmethoden von NLOGIT.

NLOGIT bietet Schätzmethoden für alle aktuellen Modelle, wie gemischte (zufällige Koeffizienten) Logit-, genestete Logit-, multinomiale Probit- und heteroskedastische Extremwert-Modelle. Bei der Wahl von Choice Set Variablen besteht vollkommene Freiheit. Daten können als einzelne Beobachtungen, Rangordnungen, Häufigkeiten oder Marktanteile vorliegen. Datensätze können außerdem revealed und stated Preferences kombinieren.
NLOGIT Version 5 ist eine Erweiterung von LIMDEP. Zusätzlich zu allen Funktionen von LIMDEP werden Methoden zur Schätzung, Modellsimulation und Analyse von Multinomialen Choice Sets bereitgestellt. Das Beinhaltet z.B. Markenpräferenzen, Transportmethoden und generell Survey- und Marktdaten bei denen der Kunde die Wahl zwischen verschiedenen Alternativen hat.

NLOGIT ist das Standardpacket zur Schätzung und Simulation von multinomialen, diskreten Choice-Modellen. In Version 5.0 wird unter anderem ein Full Information Maximum Likelihood (FIML) Schätzer für bis zu vierfach genestete Logit-Modelle zur Verfügung gestellt. Zusätzlich werden viele andere Methoden von NLOGIT unterstützt. Das beinhaltet zufällige Koeffizienten (im Rahmen von gemischten Logit-Modellen), latente Klassen, multinomiale Probit-Modelle, viele Formen von genesteten gemischten Logit-Modellen sowie verschiedene neue Methoden zur Analyse von Panel-Daten. NLOGIT 5 enthält alle Funktionen und Möglichkeiten von LIMDEP 10 sowie NLOGITs FIML Schätzalgorithmus. Als Kombination von LIMDEP und NLOGIT ist NLOGIT 5 das einzige große Programmpaket zur Analyse von diskreten Choice Sets, dass gleichzeitig den vollen Funktionsumfang eines Ökonometrieprogramms liefert.

Weitere Informationen

  Windows®
Andere Voraussetzungen DVD-ROM Laufwerk
Betriebssystem Windows XP, Vista, 7, 8 (64-Bit kompatibel)
Minimum CPU 486 Processor oder höher
Min. RAM 512 MB
Festplattenplatz 100 MB freier Speicherplatz auf der Festplatte

What's new in NLOGIT 5

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.

Model Extensions

  • 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.

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.
    These include:
    • 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.
  • Simulation
    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.

LIMDEP 10 Features

  • 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
    • Predictions
    • Partial effects for all models
    • Oaxaca decomposition
    • Simulations
  • 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
    • Heteroscedasticity
    • 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
  • Accuracy
    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.