The Econometrics Journal publishes January 2022 issue


The Econometrics Journal welcomes the new year by publishing the latest econometric theory and methods, and their applications to COVID and other societal problems, in its January 2022 issue.

Jörg Stoye's lead article studies how prevalence of a novel infection like SARS-CoV-2 can be partially identified using bounds on the selectivity and sensitivity of diagnostic tests. It demonstrates that even the weakest of the novel bounds are reasonably informative in an application to data from the early stages of the COVID-19 pandemic. For example, for Italy, they would have implied an infection fatality rate well above the one of influenza at a time (mid April 2020) when this was still subject to public speculation. The article nicely exemplifies how state-of-the-art econometrics can be used to study current and urgent societal problems. Like all earlier Editors’ Choices of lead article, it is freely available from OUP.


The issue contains two further articles with applications to COVID-19. Philipp Breidenbach and Timo Mitze's paper uses difference-in-differences, dynamic event study, and synthetic control methods to study the effects of large-scale sport events with live spectators on local COVID-19 infection trends in Germany. The article does not find effects when data on all leagues and numbers of spectators are combined. However, it concludes that first league games, in particular those with large numbers of spectators and limited mask mandates, increase infections.

Roy Cerqueti, Raffaella Coppier, Allessandro Girardi, and Marco Ventura's article introduces an augmented synthetic control method and applies it to the analysis of lockdown measures in Italy. It finds that the proposed method is better able than a standard synthetic control method to select donor countries for its synthetic control. It concludes that around 21,600 lives were saved over the first 35 days of lockdown in Italy.

The issue contains nine further articles, proposing robust rank-based GARCH model estimation, a novel Bayesian approach to synthetic control, LASSO-type estimation of high-dimensional parameters identified by single-index conditional moment restrictions, and much more. Each article illustrates its novel contributions to econometrics with an empirical application.