ABSTRACT We review developments in conducting inference for model parameters in the presence of intertemporal and cross‐sectional dependence with an emphasis on panel data applications. We review the use of heteroskedasticity and autocorrelation consistent (HAC) standard error estimators, which include the standard clustered and multiway clustered estimators, and discuss alternative sample‐splitting inference procedures, such as the Fama–Macbeth procedure, within this context. We outline pros and cons of the different procedures. We then illustrate the properties of the discussed procedures within a simulation experiment designed to mimic the type of firm‐level panel data that might be encountered in accounting and finance applications. Our conclusion, based on theoretical properties and simulation performance, is that sample‐splitting procedures with suitably chosen splits are the most likely to deliver robust inferential statements with approximately correct coverage properties in the types of large, heterogeneous panels many researchers are likely to face.

Abstract Contracting between tax entities and tax professionals occurs millions of times every year, yet little is known about the nature of these economic interactions. This study examines the effect of commonly occurring contextual factors on tax professionals’ billing decisions for tax research. These contextual factors are unrelated to the tax research itself and the time it takes to conduct the tax research, but we find that billing decisions are strongly influenced by the three non‐time‐related contextual factors that we manipulate. Initial client volume impacts amounts billed for tax research, with lower initial client volume resulting in higher per client fees. Further, we find that initial billing decisions serve as value billing benchmarks for unanticipated subsequent clients who benefit from research conducted for initial clients. As a result, subsequent clients are billed higher fees when they follow a smaller number of initial clients. We also find that client referrals are billed higher fees than nonclient referrals because professionals attempt to avoid making initial clients feel as though they have been treated unfairly relative to subsequent clients who would otherwise be billed lower fees. The results of this study are relevant beyond the traditional confines of accounting research—they are relevant to the millions of tax entities that contract with tax professionals each year.

How does transparency, a key feature of central bank design, affect monetary policy makers’ deliberations? Theory predicts a positive discipline effect and negative conformity effect. We empirically explore these effects using a natural experiment in the Federal Open Market Committee in 1993 and computational linguistics algorithms. We first find large changes in communication patterns after transparency. We then propose a difference-in-differences approach inspired by the career concerns literature, and find evidence for both effects. Finally, we construct an influence measure that suggests the discipline effect dominates.

Capturing financial network linkages and contagion in stress test models are important goals for banking supervisors and central banks responsible for micro- and macroprudential policy. However, granular data on financial networks is often lacking, and instead the networks must be reconstructed from partial data. In this paper, we conduct a horse race of network reconstruction methods using network data obtained from 25 different markets spanning 13 jurisdictions. Our contribution is two-fold: first, we collate and analyze data on a wide range of financial networks. And second, we rank the methods in terms of their ability to reconstruct the structures of links and exposures in networks.