### Archive

Archive for July 2016

## Probability differences and odds ratios measure conditional-on-covariate effects and population-parameter effects


Categories: Statistics Tags:

## Doctors versus policy analysts: Estimating the effect of interest

$$\newcommand{\Eb}{{\bf E}}$$The change in a regression function that results from an everything-else-held-equal change in a covariate defines an effect of a covariate. I am interested in estimating and interpreting effects that are conditional on the covariates and averages of effects that vary over the individuals. I illustrate that these two types of effects answer different questions. Doctors, parents, and consultants frequently ask individuals for their covariate values to make individual-specific recommendations. Policy analysts use a population-averaged effect that accounts for the variation of the effects over the individuals. Read more…

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## Effects of nonlinear models with interactions of discrete and continuous variables: Estimating, graphing, and interpreting

I want to estimate, graph, and interpret the effects of nonlinear models with interactions of continuous and discrete variables. The results I am after are not trivial, but obtaining what I want using margins, marginsplot, and factor-variable notation is straightforward. Read more…

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

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…