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统计分析
编程语言允许支持估计量的扩展:
模型的估计
测试和限制
后评估分析
模拟
局部的效果
Oaxaca分解
Delta和Krinsky/Robb方法
面板数据模型
所有的线性和非线性模型都可以用形式的面板数据来分析,包括:
固定和随机效应线性模型
非线性固定效应模型
随机效应模型
随机参数混合模型
潜在类别模型
统计&绘图
描述性统计和图形分析工具包括:
截面和面板的描述性统计
平均数和分位数表
时间序列
谱密度
图形工具
核密度
判别分析
等高线图
Complete Statistical Analysis Tools
NLOGIT 6 includes all the features and capabilities of LIMDEP 11 plus NLOGIT’s estimation and analysis tools for multinomial choice modeling.
NLOGIT software is the only large package for choice modeling that contains the full set of features of an integrated statistics program.
The Power of NLOGIT
NLOGIT 6 provides programs for estimation, 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. Since its introduction nearly 20 years ago, NLOGIT has become the premier statistical package for estimation and simulation of multinomial logit models including willingness to pay and best/worst modeling. NLOGIT is the only program available that supports mixing stated and revealed choice data sets.
Superior Analysis Tools for Multinomial Choice Modeling
Our NLOGIT statistical software provides the widest and deepest array of tools available anywhere for analysis of multinomial logit models, including nested logit, generalized mixed multinomial logit, heteroscedastic extreme value, multinomial probit, mixed logit and more. A unique simulation package that allows you to analyze alternative scenarios in the context of any estimated discrete choice model with any data set, whether used in estimation or as hold out data for examining model cross validity.
Model Estimation & Data Analysis: Ordered Choice Models
LIMDEP and NLOGIT offer extensive capabilities for ordered choice analysis including ordered probit, logit and hierarchical models, zero inflation models, partial effects, panel data and more.
Nonlinear Systems of Equations
Nonlinear least squares
Instrumental variables
Efficient GMM estimation
Loglinear Models (GLM)
Weibull regression
Inverse Gaussian regression
Exponential regression
Gamma regression
Beta regression
Binomial regression model (All are supported for cross section, fixed effects, random effects, random parameters, latent class formulations.)
Restrictions
Wald, LM, LR tests
Linear restrictions
Predictions
Heteroscedasticity
LR and LM test
Maximum likelihood estimation
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