We propose a wild bootstrap procedure for linear regression models estimated by instrumental variables. Like other bootstrap procedures that we have proposed elsewhere, it uses efficient estimates of the reduced-form equation(s). Unlike them, it takes account of possible heteroskedasticity of...

In regression models, appropriate bootstrap methods for inference robust to heteroskedasticity of unknown form are the wild bootstrap and the pairs bootstrap. The finite sample performance of a heteroskedastic-robust test is investigated with Monte Carlo experiments. The simulation results...

In the presence of heteroskedasticity of unknown form, the Ordinary Least Squares parameter estimator becomes inefficient and its covariance matrix estimator inconsistent. Eicker (1963) and White (1980) were the first to propose a robust consistent covariance matrix estimator, that permits...

Recent results of Cribari-Neto and Zarkos (1999) show that bootstrap methods can be successfully used to estimate a heteroskedasticity robust covariance matrix estimator. In this paper, we show that the wild bootstrap estimator can be calculated directly, without simulations, as it is just a...

This article gives the asymptotic properties of multivariate k-nearest neighbor regression estimators for dependent variables belonging to Rd, d 1. The results derived here permit to provide consistent forecasts, and confidence intervals for time series An illustration of the method is given...

We propose an instrumental variables method for inference in high-dimensional structural equations with endogenous regressors. The number of regressors K can be much larger than the sample size. A key ingredient is sparsity, i.e., the vector of coefficients has many zeros, or approximate...