Clustered data are common in many fields. Some prominent examples of clustering are
employees clustered within supervisors, students within classrooms, and clients within …

This paper is a practical guide (a toolkit) for researchers, students and practitioners wishing
to introduce randomization as part of a research design in the field. It first covers the …

This paper synthesizes recent advances in the econometrics of difference-in-differences
(DiD) and provides concrete recommendations for practitioners. We begin by articulating a …

This article proposes doubly robust estimators for the average treatment effect on the treated
(ATT) in difference-in-differences (DID) research designs. In contrast to alternative DID …

In this article, we consider identification, estimation, and inference procedures for treatment
effect parameters using Difference-in-Differences (DiD) with (i) multiple time periods,(ii) …

We investigated the differential diffusion of all of the verified true and false news stories
distributed on Twitter from 2006 to 2017. The data comprise~ 126,000 stories tweeted by~ 3 …

Clustered standard errors, with clusters defined by factors such as geography, are
widespread in empirical research in economics and many other disciplines. Formally …

Analyses of spatial or network data are now very common. Yet statistical inference is
challenging since unobserved heterogeneity can be correlated across neighboring …

Despite scads research on the relationship between corporate social responsibility and
financial performance, literature is still inconclusive. This study attempts to examine the …

The aim of this paper is to investigate the relationship between environmental, social, and
governance (ESG) controversies and firm market value. We use a unique dataset of more …