*! version 1.0.1 27may2026

Title

dnqr postestimation -- Postestimation tools for nqar and dnqr

Postestimation tools

The following post-estimation commands are available after a
successful nqar or dnqr fit.

dnqr_plot quantile-coefficient plot with confidence bands

dnqr_impulse tail-event impulse response (network propagation)

dnqr/nqar (without arguments) replay the stored results table

estimates save, restore, compare estimation results

Stored results consumed by the postestimation tools

Both nqar and dnqr expose the following matrices
indexed by quantile, which are the primary inputs to the postestimation
commands.

e(quantile) 1 x q vector of quantiles

e(b_q) kxq matrix of coefficients (rows are regressors)

e(se_q) kxq matrix of Powell standard errors

e(t_q) z-statistics

e(p_q) two-sided p-values

e(lo_q), e(hi_q) confidence bounds (used by dnqr_plot)

e(alphahat) (dnqr only) IVQR grid minimum per tau

Replaying results

Type nqar or dnqr with no arguments to redisplay the boxed
estimation table from the previous run, e.g.

. nqar y, network(W) rowstd q(0.1 0.5 0.9)

. nqar // redisplay

Integration with esttab / estout

dnqr and nqar are e-class commands so they cooperate with
estimates store and esttab. Because the
package fits multiple quantiles in one run, the easiest workflow
is to call the estimator separately for each tau and store the results
under tau-specific names, then combine them in a single esttab
table:

. foreach q in 0.10 0.25 0.50 0.75 0.90 {

. dnqr y, network(W) rowstd quantile(`q') notable

. estimates store dnqr_`= 100*`q'`

. }

. esttab dnqr_10 dnqr_25 dnqr_50 dnqr_75 dnqr_90,

se star(* 0.10 ** 0.05 *** 0.01) booktabs

Also see

Package TOC: dnqrlib

Estimators: nqar, dnqr

Other postestimation: dnqr_plot, dnqr_impulse

Author: Dr Merwan Roudane -
merwanroudane920@gmail.com