rmdcev: rmdcev: Estimating and simulating Kuhn-Tucker and multiple...

In rmdcev: Kuhn-Tucker and Multiple Discrete-Continuous Extreme Value Models

rmdcev: Estimating and simulating Kuhn-Tucker and multiple discrete-continuous extreme value (MDCEV) demand models

Description

The rmdcev R package estimate and simulates 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. All models are implemented in Stan, which is a C++ package for performing full Bayesian inference (see Stan Development Team, 2019) <https://mc-stan.org/>. The rmdcev package also implements demand forecasting (Pinjari and Bhat (2011) <https://repositories.lib.utexas.edu/handle/2152/23880>) and welfare calculation (Lloyd-Smith (2018) <\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.jocm.2017.12.002")}>) for policy simulation. More information on the package is provided in Lloyd-Smith (2020) <\Sexpr[results=rd]{tools:::Rd_expr_doi("10.32614/RJ-2021-015")}>).

Author(s)

Patrick Lloyd-Smith [e-mail]

References

Lloyd-Smith, P. (2020). Kuhn-Tucker and Multiple Discrete-Continuous Extreme Value (MDCEV) Model Estimation and Simulation in R: The rmdcev Package. R Journal. 12(2): 251-265. \Sexpr[results=rd]{tools:::Rd_expr_doi("doi.org/10.32614/RJ-2021-015")}(link) Bhat, CR (2008). The multiple discrete-continuous extreme value (MDCEV) model: Role of utility function parameters, identification considerations, and model extensions. Transportation Research Part B: Methodological, 42(3): 274-303.\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.trb.2007.06.002")}(link) Lloyd-Smith, P (2018). A new approach to calculating welfare measures in Kuhn-Tucker demand models. Journal of Choice Modeling 26:19–27. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.jocm.2017.12.002")}(link) Pinjari, AR, Bhat, CR (2011). Computationally Efficient Forecasting Procedures for Kuhn-Tucker Consumer Demand Model Systems: Application to Residential Energy Consumption Analysis. Department of Civil and Environmental Engineering, University of South Florida. (link) Stan Development Team (2019). RStan: the R interface to Stan. R package version 2.19.2. (link)

rmdcev documentation built on March 31, 2023, 6:49 p.m.