Quantile regression (QR) is a principal regression method for analyzing the impact of covariates on outcomes. The impact is described by the conditional quantile function and its functionals. In this paper we develop the nonparametric QR series framework, covering many regressors as a special...

This study presents patterns of productivity change in a large set of 266 public higher education institutions (HEIs) from 7 European countries across the time period 2001-2005. We adopt consistent bootstrap estimation procedures to obtain confidence intervals for Malmquist indices of HEI...

This paper studies robustness of bootstrap inference methods under moment conditions. In particular, we compare the uniform weight and implied probability bootstraps by analyzing behaviors of the bootstrap quantiles when outliers take arbitrarily large values, and derive the breakdown points for...

This article uses a sequentialized experimental design to select simulation input combinations for global optimization, based on Kriging (also called Gaussian process or spatial correlation modeling); this Kriging is used to analyze the input/output data of the simulation model (computer code)....

This paper studies the Balassa-Samuelson hypothesis in two areas with strong differences in economic development, sixteen OECD countries and sixteen Latin American economies. Applying panel cointegration and bootstrapping techniques that solve for cross-sectional dependence problems in the data,...

Stability tests for cointegrating coefficients are known to have very low power with small to medium sample sizes. In this paper we propose to solve this problem by extending the tests to dependent cointegrated panels through the stationary bootstrap. Simulation evidence shows that the proposed...

Seven bootstrap algorithms coded in SAS® are compared. The fastest (“OPDY”), which uses no modules beyond Base SAS®, achieves speed increases almost two orders of magnitude faster (over 80x faster) than the relevant "built-in” SAS® procedure (Proc SurveySelect). It is even much faster...

Since the advent of heteroskedasticity-robust standard errors, several papers have proposed adjustments to the original White formulation. We replicate earlier findings that each of these adjusted estimators performs quite poorly in finite samples. We propose a class of alternative...

The purpose of this paper is to apply the results of Brandolini D. – Colucci S. “Backtesting Value-at-Risk: A comparison between Filtered Bootstrap and Historical Simulation” in order to extend the VaR estimation also in a bond universe and particularly in order to estimate sovereign risk....

It is shown that parametric bootstrap can be used for computing P-values of goodness-of-fit tests of multivariate time series parametric models. These models include Markovian models, GARCH models with non-Gaussian innovations, regime-switching models, as well as semi parametric models involving...