Federal Reserve

Yesol Huh and Matthew Vanderpool KlingThis paper introduces parallel trends forest, a novel approach to constructing optimal control samples when using difference-in-differences (DiD) in a relatively long panel data with little randomization in treatment assignment. Our method uses machine learning techniques to construct an optimal control sample that best meet the parallel trends assumption. We demonstrate that our approach outperforms existing methods, particularly with noisy, granular data.

Anushka MitraThe unemployment rate remains elevated long after recessions, a persistence that standard search-and-matching models cannot explain. I show that noise shocks—expectational errors due to the noise in received signals about aggregate shocks—account for much of this sluggishness. Using a structural VAR, I find that absent noise shocks unemployment would have recovered to its pre-recession level six quarters earlier over 1968–2019.

Viktors StebunovsA cornerstone of the law-and-finance literature is that stronger institutions reduce financial intermediation costs. Using global data on cross-border payment costs, I show this relationship can reverse in heavily regulated sectors. Anti-money laundering risks have larger cost effects in advanced economies with strong enforcement than in developing countries with weak enforcement, despite the former having lower underlying risks.

Daniel O. Beltran and Julio L. OrtizWe explore differences in the dynamics of core inflation between Europe and North America using a Bayesian time series filter that decomposes the level of core inflation in the major advanced economies into regional, global, and country-specific components. We find a prominent role for both regional and global factors. Historically, the two regional components have at times diverged.