Network and panel quantile effects via distribution regression

This paper provides a method to construct simultaneous con fidence 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 s...

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Main Authors: Chernozhukov, V, Fernández-Val, I, Weidner, M
Format: Journal article
Language:English
Published: Elsevier 2020
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author Chernozhukov, V
Fernández-Val, I
Weidner, M
author_facet Chernozhukov, V
Fernández-Val, I
Weidner, M
author_sort Chernozhukov, V
collection OXFORD
description This paper provides a method to construct simultaneous con fidence 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 confi dence bands for distribution functions constructed from fixed effects distribution regression estimators. These fi xed effects estimators are bias corrected to deal with the incidental parameter problem. Under asymptotic sequences where both dimensions of the data set grow at the same rate, the confi dence 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.
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spelling oxford-uuid:abb43bf3-9c06-437c-8988-ac1de9f87cfb2024-05-09T09:18:04ZNetwork and panel quantile effects via distribution regressionJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:abb43bf3-9c06-437c-8988-ac1de9f87cfbEnglishSymplectic ElementsElsevier2020Chernozhukov, VFernández-Val, IWeidner, MThis paper provides a method to construct simultaneous con fidence 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 confi dence bands for distribution functions constructed from fixed effects distribution regression estimators. These fi xed effects estimators are bias corrected to deal with the incidental parameter problem. Under asymptotic sequences where both dimensions of the data set grow at the same rate, the confi dence 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.
spellingShingle Chernozhukov, V
Fernández-Val, I
Weidner, M
Network and panel quantile effects via distribution regression
title Network and panel quantile effects via distribution regression
title_full Network and panel quantile effects via distribution regression
title_fullStr Network and panel quantile effects via distribution regression
title_full_unstemmed Network and panel quantile effects via distribution regression
title_short Network and panel quantile effects via distribution regression
title_sort network and panel quantile effects via distribution regression
work_keys_str_mv AT chernozhukovv networkandpanelquantileeffectsviadistributionregression
AT fernandezvali networkandpanelquantileeffectsviadistributionregression
AT weidnerm networkandpanelquantileeffectsviadistributionregression