The bootstrap is a method for estimating the distribution of an estimator or test statistic by
resampling one's data or a model estimated from the data. Under conditions that hold in a …

The wild bootstrap was originally developed for regression models with heteroskedasticity of
unknown form. Over the past 30 years, it has been extended to models estimated by …

This paper is an effort to investigate claims concerning Granger causality relationship from
energy consumption to economic growth in South Africa. We adopt a nonparametric …

Researchers have increasingly realized the need to account for within-group dependence in
estimating standard errors of regression parameter estimates. The usual solution is to …

The cluster robust variance estimator (CRVE) relies on the number of clusters being
sufficiently large. Monte Carlo evidence suggests that the 'rule of 42'is not true for …

One puzzling results in the literature on energy consumption-economic growth causality is
the variability of results particularly across sample periods, sample sizes, and model …

The bootstrap is a method for estimating the distribution of an estimator or test statistic by
resampling one's data or a model estimated from the data. Under conditions that hold in a …

The wild bootstrap is studied in the context of regression models with heteroskedastic
disturbances. We show that, in one very specific case, perfect bootstrap inference is …

The purpose of this article is to examine the export–output nexus in Japan by taking into
account the time variation in the causal link with bootstrap Granger non-causality test and …

We present experimental evidence that promises and threats mitigate the hold‐up problem.
While investors rely as much on their own threats as on their trading partner's promises, the …