I consider nuclear norm penalized quantile regression for large N and large T panel data models with interactive fixed effects. The estimator solves a convex minimization problem, not requiring pre-estimation of the (number of the) fixed effects. Uniform rates are obtained for both the regression coefficients and the common component estimators . The rate of the latter is nearly optimal. To derive the rates, I also show new results that establish uniform bounds related to random matrices of jump processes. These results may have independent interest. Finally, I conduct Monte Carlo simulations to illustrate the estimator's finite sample performance.
I consider nuclear norm penalized quantile regression for large N and large T panel data models with interactive fixed effects. The estimator solves a convex minimization problem, not requiring pre-estimation of the (number of the) fixed effects. Uniform rates are obtained for both the slope coefficients and the low-rank common component of the interactive fixed effects . The rate of the latter is nearly optimal. To derive the rates, I show new results that establish uniform bounds of norms of certain random matrices of jump processes. These results may have independent interest. Finally, I conduct Monte Carlo simulations to illustrate the estimator's finite sample performance.
R
regression
slope
R
common component estimators
low-rank common component of the interactive fixed effects
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also
R
related to
of norms of certain