Bayesian Distribution Regression

49 Pages Posted: 6 Oct 2017 Last revised: 6 Apr 2019

See all articles by Weige Huang

Weige Huang

Weige Huang

Emmanuel Tsyawo

Temple University, College of Liberal Arts, Department of Economics

Date Written: April 2019

Abstract

This paper introduces a Bayesian version of distribution regression that enables inference on estimated distributions, quantiles, distributional effects, among other functionals of interest. Our estimators come in three categories: the non-asymptotic, semi-asymptotic, and asymptotic. To conduct simultaneous inference on a function of any estimator, we introduce asymmetric and symmetric Bayesian confidence bands. Inference on point estimates is conducted via posterior intervals. The Bayesian asymptotic theory we develop extends the foregoing to gains in computational time and tractability of posterior distributions. Monte Carlo simulations conducted illustrate good performance of our estimators. We apply our estimators to evaluate the impact of institutional ownership on firm innovation.

Keywords: Distribution regression, Counterfactual analysis, Bayesian inference, Simultaneous confidence bands

JEL Classification: C11, D22

Suggested Citation

Huang, Weige and Tsyawo, Emmanuel, Bayesian Distribution Regression (April 2019). Available at SSRN: https://ssrn.com/abstract=3048658 or http://dx.doi.org/10.2139/ssrn.3048658

Weige Huang

Weige Huang ( email )

United States

Emmanuel Tsyawo (Contact Author)

Temple University, College of Liberal Arts, Department of Economics ( email )

Philadelphia, PA 19122
United States

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