Despite much recent work on the finite-sample properties of estimators and tests for linear regression models with a single endogenous regressor and weak instruments, little attention has been paid to tests for overidentifying restrictions in these circumstances. We study asymptotic tests for...

New macro empirical evidence is provided to assess the relative importance of object and idea gaps in explaining the world income distribution dynamics over a benchmark period 1960-1985. Results are then extended through 1995. Formal statistical hypothesis tests allow us to discriminate between...

We propose bootstrap implementations of the asymptotic Wald, likelihood ratio and Lagrange multiplier tests for the order of integration of a fractionally integrated time series. Our main purpose in doing so is to develop tests which are robust to both conditional and unconditional...

Inference using difference-in-differences with clustered data requires care. Previous research has shown that t tests based on a cluster-robust variance estimator (CRVE) severely over-reject when there are few treated clusters, that different variants of the wild cluster bootstrap can...

This paper applies the tool box measures of disaggregative income inequality characterization and the statistical methodology of Beach (2021) to percentile-based distribution statistics such as quintile income shares and decile means typically published by official statistical agencies. It...

This paper extends the statistical inference approach developed in Beach (2016) to look at income changes over different regions of an income distribution. Specifically, it looks at relative-mean earnings (RME) ratios and mean earnings levels for lower earners, middle-class (MC) workers and...

This paper offers a tool box of disaggregative measures of distributional change, including population shares, income shares, quantile mean incomes and relative mean incomes of different income groups. It highlights median-based measures along with quintiles and deciles. It also offers formulas...

Methods for cluster-robust inference are routinely used in economics and many other disciplines. However, it is only recently that theoretical foundations for the use of these methods in many empirically relevant situations have been developed. In this paper, we use these theoretical results to...

As I document using evidence from a journal data repository that I manage, the datasets used in empirical work are getting larger. When we use very large datasets, it can be dangerous to rely on standard methods for statistical inference. In addition, we need to worry about computational issues....

Inference using difference-in-differences with clustered data requires care. Previous research has shown that, when there are few treated clusters, t-tests based on cluster-robust variance estimators (CRVEs) severely overreject, and different variants of the wild cluster bootstrap can either...