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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.
Authors
Research Associate University College London and University of Oxford
Martin is an IFS Research Associate, a Fellow of the Nuffield College and a Professor in the Department of Economics at the University of Oxford.
Ivan Fernandez-Val
Working Paper details
- DOI
- 10.1920/wp.cem.2020.2720
- Publisher
- The IFS
Suggested citation
V, Chernozhukov and I, Fernandez-Val and M, Weidner. (2020). Network and Panel Quantile Effects Via Distribution Regression. London: The IFS. Available at: https://ifs.org.uk/publications/network-and-panel-quantile-effects-distribution-regression-1 (accessed: 18 April 2024).
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