Network and Panel Quantile Effects Via Distribution Regression

Victor Chernozhukov, Ivan Fernandez-Val () and Martin Weidner ()
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Martin Weidner: Institute for Fiscal Studies and University College London

No CWP27/20, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

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.

Date: 2020-06-15
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Related works:
Journal Article: Network and panel quantile effects via distribution regression (2024)
Working Paper: Network and Panel Quantile Effects Via Distribution Regression (2020)
Working Paper: Network and panel quantile effects via distribution regression (2018)
Working Paper: Network and panel quantile effects via distribution regression (2018)
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