A sample of 250 Californian households

datasets

Maximum likelihood estimation of random utility discrete
choice models. The software is described in Croissant (2020)
<doi:10.18637/jss.v095.i11> and the underlying methods in
Train (2009) <doi:10.1017/CBO9780511805271>.

Yves Croissant

mlogit

Multinomial Logit Models

HC function

A dataframe containing :<ul>
<li>depvar: heating system, one of <code>gcc</code> (gas central heat with
cooling), <code>ecc</code> (electric central resistence heat with cooling), <code>erc</code>
(electric room resistence heat with cooling), <code>hpc</code> (electric heat
pump which provides cooling also), <code>gc</code> (gas central heat without
cooling), <code>ec</code> (electric central resistence heat without cooling), <code>er</code>
(electric room resistence heat without cooling),</li>
<li>ich.z: installation cost of the heating portion of the
system,</li>
<li>icca: installation cost for cooling,</li>
<li>och.z: operating cost for the heating portion of the system,</li>
<li>occa: operating cost for cooling,</li>
<li>income: annual income of the household.</li>
</ul>

HC: Heating and Cooling System Choice in Newly Built Houses in California

Description

Arguments

Format