gmnl - Multinomial Logit Models with Random Parameters

An implementation of maximum simulated likelihood method for the estimation of multinomial logit models with random coefficients as presented by Sarrias and Daziano (2017) <doi:10.18637/jss.v079.i02>. Specifically, it allows estimating models with continuous heterogeneity such as the mixed multinomial logit and the generalized multinomial logit. It also allows estimating models with discrete heterogeneity such as the latent class and the mixed-mixed multinomial logit model.

Last updated 3 years ago

4.11 score 4 stars 50 scripts 1.6k downloads

Rchoice - Discrete Choice (Binary, Poisson and Ordered) Models with Random Parameters

An implementation of simulated maximum likelihood method for the estimation of Binary (Probit and Logit), Ordered (Probit and Logit) and Poisson models with random parameters for cross-sectional and longitudinal data as presented in Sarrias (2016) <doi:10.18637/jss.v074.i10>.

Last updated 2 years ago

3.92 score 4 stars 34 scripts 1.2k downloads

spldv - Spatial Models for Limited Dependent Variables

The current version of this package estimates spatial autoregressive models for binary dependent variables using GMM estimators <doi:10.18637/jss.v107.i08> and RIS estimator <doi:10.1007/978-3-662-05617-2_8>. It supports one-step (Pinkse and Slade, 1998) <doi:10.1016/S0304-4076(97)00097-3> and two-step GMM estimator along with the linearized GMM estimator proposed by Klier and McMillen (2008) <doi:10.1198/073500107000000188>. It also allows for either Probit or Logit model and compute the average marginal effects. The RIS estimator allows to estimate the SAR and SEM model. All these models are presented in Sarrias and Piras (2023) <doi:10.1016/j.jocm.2023.100432>.

Last updated 6 months ago

2.78 score 1 stars 1 packages 191 downloads