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 …

In many fields of economics, and also in other disciplines, it is hard to justify the assumption
that the random error terms in regression models are uncorrelated. It seems more plausible …

In spite of a sizeable body of research linking employee loyalty to organizational
performance, the causal link between employee loyalty and employee wages has rarely …

Efficient computational algorithms for bootstrapping linear regression models with clustered
data are discussed. For ordinary least squares (OLS) regression, a new algorithm is …

Duflo (2001) exploits a 1970s schooling expansion to estimate the returns to schooling in
Indonesia. Under the study's difference-in-differences (DID) design, two patterns in the data …

Some popular views about the workings of the economy are completely at odds with solid
empirical evidence and congruent theoretical explanations and therefore can be qualified as …

This paper seeks to understand household business decisions in response to increased
credit access in an environment with multiple market failures. A simple model suggests that …

We study the wild bootstrap inference for instrumental variable regressions under an
alternative asymptotic framework that the number of independent clusters is fixed, the size of …

Abstract Research Summary Literature that compares the advantages of financial hedging
and operational flexibility as instruments to manage exchange‐rate uncertainty presents …

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