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Flexible discrete choice modeling using a multinomial probit model, part 2

5 July 2016 No comments

Overview

In the first part of this post, I discussed the multinomial probit model from a random utility model perspective. In this part, we will have a closer look at how to interpret our estimation results.

How do we interpret our estimation results?

We created a fictitious dataset of individuals who were presented a set of three health insurance plans (Sickmaster, Allgood, and Cowboy Health). We pretended to have a random sample of 20- to 60-year-old persons who were asked Read more…

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Flexible discrete choice modeling using a multinomial probit model, part 1

28 June 2016 3 comments

\(\newcommand{\xb}{{\bf x}}
\newcommand{\betab}{\boldsymbol{\beta}}
\newcommand{\zb}{{\bf z}}
\newcommand{\gammab}{\boldsymbol{\gamma}}\)We have no choice but to choose

We make choices every day, and often these choices are made among a finite number of potential alternatives. For example, do we take the car or ride a bike to get to work? Will we have dinner at home or eat out, and if we eat out, where do we go? Scientists, marketing analysts, or political consultants, to name a few, wish to find out why people choose what they choose.

In this post, Read more…

Categories: Statistics Tags: , , , , , ,