dd_est_hmnl

Estimate discrete choice model (HMNL)

Implements choice models based on economic theory, including estimation using Markov chain Monte Carlo (MCMC), prediction, and more. Its usability is inspired by ideas from 'tidyverse'. Models include versions of the Hierarchical Multinomial Logit and Multiple Discrete-Continous (Volumetric) models with and without screening. The foundations of these models are described in Allenby, Hardt and Rossi (2019) <doi:10.1016/bs.hem.2019.04.002>. Models with conjunctive screening are described in Kim, Hardt, Kim and Allenby (2022) <doi:10.1016/j.ijresmar.2022.04.001>. Models with set-size variation are described in Hardt and Kurz (2020) <doi:10.2139/ssrn.3418383>.

Nino Hardt

echoice2

Choice Models with Economic Foundation

dd_est_hmnl function

<dl><dt>dd</dt>
<dd>discrete choice data (long format)</dd>
<dt>R</dt>
<dd>draws</dd>
<dt>keep</dt>
<dd>thinning</dd>
<dt>cores</dt>
<dd>no of CPU cores to use (default: auto-detect)</dd>
<dt>control</dt>
<dd>list containing additional settings</dd></dl>

Arguments

Estimate discrete choice model (HMNL) — dd_est_hmnl

<dl>

<dt>dd</dt>
<dd>discrete choice data (long format)</dd>


<dt>R</dt>
<dd>draws</dd>


<dt>keep</dt>
<dd>thinning</dd>


<dt>cores</dt>
<dd>no of CPU cores to use (default: auto-detect)</dd>


<dt>control</dt>
<dd>list containing additional settings</dd>

</dl>

dd_est_hmnl: Estimate discrete choice model (HMNL)

Description

Usage

Value

Arguments

See Also

Examples