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Kernel density estimation for undirected dyadic data
Graham, Bryan S.; Niu, Fengshi; Powell, James - 2019
We study nonparametric estimation of density functions for undirected dyadic random variables (i.e., random variables de?ned for all unordered pairs of agents/nodes in a weighted network of order N). These random variables satisfy a local dependence property: any random variables in the network...
Persistent link: https://www.econbiz.de/10012146391
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Identification and efficiency bounds for the average match function under conditionally exogenous matching
Graham, Bryan S.; Imbens, Guido W.; Ridder, Geert - 2016
following matching mechanism: take a random draw from the W = wj subgroup of the first population and match her with an …), denote the expected output associated with this match. We show that (i) the AMF is identified when matching is conditionally … exogenous, (ii) conditionally exogenous matching is compatible with a pairwise stable aggregate matching equilibrium under …
Persistent link: https://www.econbiz.de/10011445793
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A quantile correlated random coefficients panel data model
Graham, Bryan S.; Hahn, Jinyong; Poirier, Alexandre; … - 2016
We propose a generalization of the linear quantile regression model to accommodate possibilities afforded by panel data. Specifically, we extend the correlated random coefficients representation of linear quantile regression (e.g., Koenker, 2005; Section 2.6). We show that panel data allows the...
Persistent link: https://www.econbiz.de/10011594348
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Semiparametrically efficient estimation of the average linear regression function
Graham, Bryan S.; Pinto, Cristine Campos de Xavier - 2018
familiar methods of covariate adjustment used for program evaluation as well as methods of semiparametric regression (e.g., the …
Persistent link: https://www.econbiz.de/10011941544
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Minimax risk and uniform convergence rates for nonparametric dyadic regression
Graham, Bryan S.; Niu, Fengshi; Powell, James - 2021
Let i = 1, . . . , N index a simple random sample of units drawn from some large population. For each unit we observe the vector of regressors Xi and, for each of the N (N - 1) ordered pairs of units, an outcome Yij . The outcomes Yij and Ykl are independent if their indices are disjoint, but...
Persistent link: https://www.econbiz.de/10012621147
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Quantile regression with panel data
Graham, Bryan S.; Hahn, Jinyong; Poirier, Alexandre; … - 2015
We propose a generalization of the linear quantile regression model to accommodate possibilities afforded by panel data. Specifically, we extend the correlated random coefficients representation of linear quantile regression (e.g., Koenker, 2005; Section 2.6). We show that panel data allows the...
Persistent link: https://www.econbiz.de/10011445722