Multinomial logit - many specifications
Random effects MNL

• Multinomial logit specification
• Choice specific constants
• Random effects and random parameters
• Choice specific attributes and interactions of characteristics with constants
• Marginal effects
• Test procedure for IIA
• Restricted choice sets
• Estimation using revealed preference or sets of ranks
• Merge stated and revealed preference data sets

Nested logit
Generalized nested logit

• Up to four levels in nested logit models
• Command builder for tree specification
• Constrained IV parameters
• Marginal 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
• Generalized nested logit allows choices to appear in multiple branches
• GNL with probabilistic allocations of choices to alternatives

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
Kernel logit

• Up to 100 random parameters
• Maximum simulated likelihood estimation
• Pseudorandom draws or Halton sequences
• Mixture of random and nonrandom parameters
• Panel data structures
• Time invariant random effects
• AR(1) specification for random components
• Freely correlated random parameters
• Unrestricted mixture of normal, lognormal, tent, uniform parameters
• Restrictions on means and/or variances of random parameters
• Individual heterogeneity in means of random parameters
• Individual specific parameter estimates
• Error components logit allows choice specific random effects
• Error components logit with stochastic specifications for nesting structures

Heteroscedastic extreme value

• Choice specific variances in MNL model
• Equality restrictions and grouping choices
• IIA test
• Homogeneity of variances test

Covariance heterogeneity

• Extends two level nested logit model
• Individual specific heteroscedasticity and heterogeneity in IV parameters

Latent class

• Multinomial logit structure
• MNL sub model for class probabilities
• Panel data structure
• Up to five latent classes