We propose a term structure model featuring double-autoregressive dynamics, which can accommodate unit roots and cointegrating relations while maintaining stationarity. In an empirical application, the model reduces in-sample misspecification and significantly improves out-of-sample yield forecasts compared with term structure models based on linear VAR dynamics. The double-autoregressive process implies term premia that resemble the term spread. We test whether these premia help in overcoming the persistence problem of stationary VAR models with mixed results. Finally, we discuss alternative interpretations of the mechanism inducing stationarity in our model.

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).

We examine the economic impact of high-yielding crop varieties (HYVs) in developing countries 1960-2000. We use time variation in the development and diffusion of HYVs of 10 major crops, spatial variation in agro-climatically suitability for growing them, and a differences-in-differences strategy to identify the causal effects of adoption. In a sample of 84 counties, we estimate that a 10 percentage points increase in HYV adoption increases GDP per capita by about 15 percent. This effect is fully accounted for by the direct effect on crop yields, factor adjustment, and structural transformation. We also find that HYV adoption reduced both fertility and mortality.

ABSTRACT Our study is motivated by the theory of credence goods in the auditing setting. We propose that audit committee accounting expertise should reduce information asymmetries between the auditor and the client, thereby limiting auditors' ability to over-audit and under-audit. Consistent with this notion, our results indicate that when audit committees have accounting expertise, clients (1) pay lower fees when changes in standards decrease required audit effort; (2) pay a smaller fee premium in the presence of remediated material weaknesses; and (3) have a reduced likelihood of restatement when audit market competition is high. Our findings in the under-auditing setting generally are strongest among non-Big 4 engagements, consistent with non-Big 4 auditors being less sensitive to market-wide disciplining mechanisms such as reputation, legal liability, and professional regulation. We also provide evidence that the nature of audit committee members' accounting expertise differentially impacts the committee's ability to curtail over- and under-auditing. JEL Classifications: M40; M41; M42.