The COVID-19 pandemic that we are experiencing is both tragic and shocking. There is no question that, except in some Asian countries trained by prior infectious outbreaks, most policy makers around the world have been ill-prepared to respond to the crisis. The effects of the coronavirus on our mental and physical health has been indeed calamitous, and the economic and financial impacts for many have been truly unfortunate. Furthermore, the extreme nature of the event is challenging researchers to compile and interpret new evidence that is arriving at a rapid pace. The editors Hui Chen, Thierry Foucault, Jeffrey Pontiff, and Nikolai Roussanov and contributing authors are to be commended for assembling and collating a thought-provoking collection of papers. More time and study will be needed to fully sift through the evidence and to glean the lessons to be learned from this pandemic for policy makers and investors. But the evidence and insights in this volume are a very good start.

I. Smuggling and welfare, 173. — II. Exogenously specified objectives: target increase in importable production, 184. — III. Overinvoicing and underinvoicing of transactions, 186. — IV. Conclusions, 187.

This article suggests that the lack of use of rights offerings in the United States, a phenomenon referred to as the equity underwriting paradox, can be explained by transaction costs. A sample of underwritten rights offerings provides support for the explanation. Firms making underwritten rights offerings paid lower underwriter fees but incurred significantly larger price drops just prior to the offering than did firms making underwritten offerings. Further analysis reveals that the underwritten-rights-offering price concessions are a form of transaction cost that is not found in underwritten public offerings.

This study examines the ability of the linear-rational square-root model to simultaneously capture cross-sectional and time-series dynamics of bond yields and their variances. The preferred model specification comprises five factors, two of which are not spanned by the yield curve, introducing unspanned stochastic volatility (USV). This specification provides a close in-sample fit to yields and yield variances, emphasizing the need for USV. Out-of-sample testing demonstrates low variance forecast errors. The specification provides evidence of USV in conditional yield variance and bond risk premia, linked to macroeconomic uncertainty.

We identify the labor market returns to university grade point average (GPA) by leveraging a nationwide change in the scaling of grades in Danish universities. Our results show that a reform-induced increase in GPA that is unrelated to ability causes higher earnings immediately after graduation, but the effect fades in subsequent years. The effect at labor market entry is largest for individuals with fewer alternative signals. Although employers initially screen candidates on the basis of skill signals, our findings are consistent with a model in which employers rapidly learn about worker productivity.

Robert G. Hansen, John R. Lott, Jr., Externalities and Corporate Objectives in a World with Diversified Shareholder/Consumers, The Journal of Financial and Quantitative Analysis, Vol. 31, No. 1 (Mar., 1996), pp. 43-68

We provide a new and more complete analysis of the origins of the Dust Bowl of the 1930s, one of the most severe environmental crises in North America in the twentieth century. Severe drought and wind erosion hit the Great Plains in 1930 and lasted through 1940. There were similar droughts in the 1950s and 1970s, but no comparable level of wind erosion. We explain why. The prevalence of small farms in the 1930s limited private solutions for controlling the downwind externalities associated with wind erosion. Drifting sand from unprotected fields damaged neighboring farms. Small farmers cultivated more of their land and were less likely to invest in erosion control than larger farmers. Soil conservation districts, established by the government after 1937, helped coordinate erosion control. This “unitized” solution for collective action is similar to that used in other natural resource/environmental settings.

This essay examines the problem of inference within a rational expectations model from two perspectives: that of an econometrician and that of the economic agents within the model. The assumption of rational expectations has been and remains an important component to quantitative research. It endows economic decision makers with knowledge of the probability law implied by the economic model. As such, it is an equilibrium concept. Imposing rational expectations removed from consideration the need for separately specifying beliefs or subjective components of uncertainty. Thus, it simplified model specification and implied an array of testable implications that are different from those considered previously. It reframed policy analysis by questioning the effectiveness of policy levers that induce outcomes that differ systematically from individual beliefs.

We introduce a novel parametrization of the correlation matrix. The reparametrization facilitates modeling of correlation and covariance matrices by an unrestricted vector, where positive definiteness is an innate property. This parametrization can be viewed as a generalization of Fisher's Z ‐transformation to higher dimensions and has a wide range of potential applications. An algorithm for reconstructing the unique n × n correlation matrix from any vector in <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" display="inline"> <a:msup> <a:mrow> <a:mi mathvariant="double-struck">R</a:mi> </a:mrow> <a:mrow> <a:mi>n</a:mi> <a:mo stretchy="false">(</a:mo> <a:mi>n</a:mi> <a:mo>−</a:mo> <a:mn>1</a:mn> <a:mo stretchy="false">)</a:mo> <a:mo stretchy="false">/</a:mo> <a:mn>2</a:mn> </a:mrow> </a:msup> </a:math> is provided, and we derive its numerical complexity.

This paper develops inference methods for the iterated overidentified Generalized Method of Moments (GMM) estimator. We provide conditions for the existence of the iterated estimator and an asymptotic distribution theory, which allows for mild misspecification. Moment misspecification causes bias in conventional GMM variance estimators, which can lead to severely oversized hypothesis tests. We show how to consistently estimate the correct asymptotic variance matrix. Our simulation results show that our methods are properly sized under both correct specification and mild to moderate misspecification. We illustrate the method with an application to the model of Acemoglu, Johnson, Robinson, and Yared (2008).