maxlikeMDCEV

data for model formatted from processMDCEVdata

stan_data

The default for fixed and random parameter specifications is to use random starting values.
For LC models, the default is to use slightly adjusted MLE point estimates from the single class model.
Initial parameter values should be included in a named list. For example, the LC "hybrid" specification
initial parameters can be specified as:
initial.parameters = list(psi = array(0, dim = c(K, num_psi)),
 gamma = array(1, dim = c(K, num_alt)),
 alpha = array(0.5, dim = c(K, 0)),
 scale = array(1, dim = c(K)))
where K is the number of classes (i.e. K = 1 is used for single class models),
num_psi is number of psi parameters, and num_alt is number of non-numeraire alternatives.

initial.parameters

mle_options

parms_info

Additional parameters to pass on to <code>rstan::stan</code>
and <code>rstan::sampling</code>.

Estimates and simulates Kuhn-Tucker demand models with individual heterogeneity. The package implements the multiple-discrete continuous extreme value (MDCEV) model and the 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 package also implements demand forecasting (Pinjari and Bhat (2011) <https://repositories.lib.utexas.edu/handle/2152/23880>) and welfare calculation (Lloyd-Smith (2018) <doi:10.1016/j.jocm.2017.12.002>) for policy simulation.

maxlikeMDCEV: maxlikeMDCEV

Description

Usage

Arguments