Multinomial Choice in NLOGIT: Model Estimation
NLOGIT supports a wide variety of specifications for discrete choice modeling.
Multinomial Logit - Many Specifications
Random Effects MNL
- Multinomial logit specification
- Scaled multinomial logit
- Random regret
- Nonlinear utility functions
- Individual specific choice sets
- Random effects and random parameters
- Fixed effects
- Choice specific attributes and interactions of characteristics with constants
- Attribute nonattendance
- Partial effects and elasticities
- Test procedures for IIA
- Restricted choice sets
- Estimation using revealed preference, sets of ranks or best/worst
- Merge stated and revealed preference data sets
Nested Logit
Generalized Nested Logit
- Up to four levels in nested logit models
- Constrained inclusive value parameters
- Partial effects decomposed at the levels in the tree
- Save utilities, inclusive values, probabilities
- FIML or two step estimation
- Random utility specifications to constrain the model
Multinomial Probit
- Up to 20 choices
- GHK simulator
- Unrestricted or restricted correlation matrix
- IIA test
- Heteroscedasticity and covariance heterogeneity
- Panel data - multinomial, multiperiod probit
Mixed (Random Parameters) Logit
- Up to 100 random parameters
- Maximum simulated likelihood estimation
- Pseudorandom draws or Halton sequences
- Mixture of random and nonrandom parameters
- Panel data structures - random effects
- Stated choice data
- Freely correlated random parameters
- Unrestricted mixture of normal, lognormal, triangular, uniform and other distributions
- Restrictions on means and/or variances of random parameters
- Individual heterogeneity in means and variances of random parameters
- Individual specific parameter estimates
- Error components logit allows choice specific random effects
- Generalized mixed logit
- Latent class mixed logit
- Estimation in willingness to pay space
- Nonlinear utility functions
Heteroscedastic Extreme Value
- Choice specific variances in MNL model
- Equality restrictions and grouping choices
- Homogeneity of variances test
Latent Class
- Multinomial logit structure
- MNL sub model for class probabilities
- Panel data structure
- Up to 30 latent classes
- Random parameters latent class model
- Cross class restrictions on parameters