The Regression Kink (RK) design is an increasingly popular empirical method, with more than 20 studies circulated using … these estimates, which typically use local linear regression, are highly sensitive to curvature in the underlying …. In contrast, our permutation test reinforces the asymptotic inference results of a recent Regression Discontinuity study …

testing the conditional independence assumption because they are consistent under the null hypothesis. We propose tests of the … Kolmogorov-Smirnov type based on the conditional quantile regression process. Monte Carlo simulations show that their size is …

The Regression Kink (RK) design is an increasingly popular empirical method, with more than 20 studies circulated using … these estimates, which typically use local linear regression, are highly sensitive to curvature in the underlying …. In contrast, our permutation test reinforces the asymptotic inference results of a recent Regression Discontinuity study …

In this paper, we consider sieve instrumental variable quantile regression (IVQR) estimation of functional coefficient … nonparametric specification test for the constancy of the functional coefficients, study its asymptotic properties under the null … and test statistic. As an empirical illustration of our theoretical results, we present the estimation of quantile Engel …

coefficients are permitted to be nonparametric functions of some contextual variables to allow for potential nonlinearities and …-specific spatial dependence measure conditional on the vector of contextual variables. We propose several (locally) nonparametric GMM …

combining together the quantile regression t-statistics from each considered quantile level and uses Monte Carlo resampling …

a nonparametric estimation method using penalized splines, which achieves the same optimal convergence rate as kernel …This paper considers estimation and inference for varying-coefficient models with nonstationary regressors. We propose … likelihood ratio test statistic for checking the stability of the regression coefficients. We derive both the exact and the …

We study the problem of estimating the parameters of a linear median regression without any assumption on the shape of … through a reverse inference approach, based on a distribution-free testing theory [Coudin and Dufour (2009, The Econometrics … Journal)], from which confidence sets and point estimators are subsequently generated. The estimation problem is tackled in …

We use local polynomial fitting to estimate the nonparametric M-regression function for strongly mixing stationary … results to the estimation of an additive M-regression model … regression function and its derivatives. These results are fundamental for statistical inference and for applications that …

In this paper, we analyze the nonparametric part of a partially linear model when the covariates in parametric and non-parametric …