Network and panel quantile effects via distribution regression

Victor Chernozhukov, Iván Fernández-Val and Martin Weidner

Journal of Econometrics, 2024, vol. 240, issue 2

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.

Keywords: Quantile effects; Counterfactual distributions; Fixed effects; Incidental parameter problem; Long panels (search for similar items in EconPapers)
Date: 2024
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Related works:
Working Paper: Network and Panel Quantile Effects Via Distribution Regression (2020)
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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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:240:y:2024:i:2:s0304407620303390

DOI: 10.1016/j.jeconom.2020.08.009

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