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B1789
Title: Network and panel quantile effects via distribution regression Authors:  Victor Chernozhukov - MIT (United States)
Ivan Fernandez-Val - Boston University (United States)
Martin Weidner - University College London (United Kingdom) [presenting]
Abstract: A method is provided 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 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 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.