Network and panel quantile effects via distribution regression
Name
1-s2.0-S0304407620303390-main.pdf
Description
Published version
Size
972.56 KB
Format
Adobe PDF
Checksum (MD5)
aa0e4cde6917d108e0528d8e1e401e23
Author(s)
Chernozhukov, Victor
Fernández-Val, Iván
Weidner, Martin
Date Issued
2020
Journal
Journal of Econometrics
Publisher
Elsevier BV
Version
Final published version
Abstract
© 2020 The Authors 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.
MIT Department
Massachusetts Institute of Technology. Department of Economics
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/J.JECONOM.2020.08.009
