DNQR: Stata module to perform Dynamic Network Quantile Regression

Roudane, Merwan

DNQR: Stata module to perform Dynamic Network Quantile Regression

Statistical Software Components from Boston College Department of Economics

Abstract: dnqr estimates the Dynamic Network Quantile Regression model of Xu, Wang, Shin and Zheng (2024). For each quantile tau in (0,1) the conditional quantile of depvar given the information set is Q{sub:Y}(tau | F_t) = gamma{sub:0}(tau) + Z'alpha(tau) + gamma{sub:1}(tau) (1/n_i) sum_j a_{ij} Y_{j,t} + gamma{sub:2}(tau) (1/n_i) sum_j a_{ij} Y_{j,t-1} + gamma{sub:3}(tau) Y_{i,t-1} + sum_{k=0..p} F_{t-k}' beta_k(tau) The contemporaneous network mean sum_j w_{ij} Y_{jt} is endogenous under the simultaneous-spillover assumption. Plugging it directly into qreg would produce an inconsistent estimator. dnqr therefore follows the instrumental variable quantile regression (IVQR) approach of Chernozhukov and Hansen (2006) and estimates gamma{sub:1}(tau) by a one-dimensional grid search: for each candidate alpha, the working quantile regression of Y - alpha * W Y_t on (instruments, exogenous regressors) is fit and the L{sub:2} norm of the instrument coefficients is computed; alpha-hat is the minimiser. Standard errors follow Powell (1986) sandwich with Hall-Sheather or Bofinger bandwidth (Koenker-Xiao 2006).

Language: Stata
Requires: Stata version 13
Keywords: quantile regression; dynamic regression (search for similar items in EconPapers)
Date: 2026-05-30
Note: This module should be installed from within Stata by typing "ssc install dnqr". The module is made available under terms of the GPL v3 (https://www.gnu.org/licenses/gpl-3.0.txt). Windows users should not attempt to download these files with a web browser.
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