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Distribution regression made easy

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  • Philippe Van Kerm

    (Luxembourg Institute of Socio-Economic Research (LISER), Luxembourg)

Abstract

Incorporating covariates in (income or wage) distribution analysis typically involves estimating conditional distribution models, that is, models for the cumulative distribution of the outcome of interest conditionally on the value of a set of covariates. A simple strategy is to estimate a series of binary outcome regression models for F(z|xi)=Pr(yi≤z|xi) for a grid of values for z (Peracchi and Foresi, 1995, Journal of the American Statistical Association; Chernozhukov et al., 2013, Econometrica) This approach now often referred to as "distribution regression" is attractive and easy to implement. This talk illustrates how the Stata commands margins and suest can be useful for inference here and suggests various tips and tricks to speed up the process and solve potential computational issues. It also shows how to use conditional distribution model estimates to analyze various aspects of unconditional distributions.

Suggested Citation

  • Philippe Van Kerm, 2016. "Distribution regression made easy," United Kingdom Stata Users' Group Meetings 2016 13, Stata Users Group.
    • Handle: RePEc:boc:usug16:13
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