Network and Panel Quantile Effects Via Distribution Regression

TL;DR

This paper introduces a method for constructing simultaneous confidence bands for quantile functions and effects in complex network and panel models with unobserved effects and discrete outcomes, ensuring valid inference in large samples.

Contribution

It develops a novel approach combining distribution regression estimators with debiasing techniques to handle unobserved effects and discrete outcomes in network and panel data.

Findings

Confidence bands achieve correct joint coverage asymptotically.

Method successfully applied to gravity models of trade.

Handles unobserved two-way effects and discrete outcomes.

Abstract

This paper provides a method to construct simultaneous confidence bands for quantile functions and quantile effects in nonlinear network and panel models with unobserved two-way effects, strictly exogenous covariates, and possibly discrete outcome variables. The method is based upon projection of simultaneous confidence bands for distribution functions constructed from fixed effects distribution regression estimators. These fixed effects estimators are debiased to deal with the incidental parameter problem. Under asymptotic sequences where both dimensions of the data set grow at the same rate, the confidence bands for the quantile functions and effects have correct joint coverage in large samples. An empirical application to gravity models of trade illustrates the applicability of the methods to network data.