The R Journal: article published in 2020, volume 12:2

Kuhn-Tucker and Multiple Discrete-Continuous Extreme Value Model Estimation and Simulation in R: The rmdcev Package
Patrick Lloyd-Smith , The R Journal (2020) 12:2, pages 251-265.

Abstract This paper introduces the package rmdcev in R for estimation and simulation of Kuhn Tucker demand models with individual heterogeneity. The models supported by rmdcev are the multiple-discrete continuous extreme value (MDCEV) model and Kuhn-Tucker specification common in the environmental economics literature on recreation demand. Latent class and random parameters specifications can be implemented and the models are fit using maximum likelihood estimation or Bayesian estimation. The rmdcev package also implements demand forecasting and welfare calculation for policy simulation. The purpose of this paper is to describe the model estimation and simulation framework and to demonstrate the functionalities of rmdcev using real datasets.

Received: 2020-04-01; online 2021-01-15, supplementary material, (1.6 KiB)
CRAN packages: rmdcev, apollo, mlogit, gmnl, Formula, rstan, bayesplot, shinystan, parallel
CRAN Task Views implied by cited CRAN packages: Econometrics, Bayesian, SocialSciences

This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2021-015,
  author = {Patrick Lloyd-Smith},
  title = {{Kuhn-Tucker and Multiple Discrete-Continuous Extreme Value
          Model Estimation and Simulation in R: The rmdcev
          Package}},
  year = {2021},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2021-015},
  url = {https://doi.org/10.32614/RJ-2021-015},
  pages = {251--265},
  volume = {12},
  number = {2}
}