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.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2021-015.zip
rmdcev, apollo, mlogit, gmnl, Formula, rstan, bayesplot, shinystan, parallel
Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".
For attribution, please cite this work as
Lloyd-Smith, "The R Journal: Kuhn-Tucker and Multiple Discrete-Continuous Extreme Value Model Estimation and Simulation in R: The rmdcev Package", The R Journal, 2021
BibTeX citation
@article{RJ-2021-015,
author = {Lloyd-Smith, Patrick},
title = {The R Journal: Kuhn-Tucker and Multiple Discrete-Continuous Extreme Value Model Estimation and Simulation in R: The rmdcev Package},
journal = {The R Journal},
year = {2021},
note = {https://doi.org/10.32614/RJ-2021-015},
doi = {10.32614/RJ-2021-015},
volume = {12},
issue = {2},
issn = {2073-4859},
pages = {251-265}
}