Dynamic Factor Demand Models and Productivity Analysis

PRODUCTIVITY GROWTH; DYNAMIC FACTOR DEMAND; SPLILLOVER; R&D; TAX INCENTIIVES; CAPITAL UTILIZATION; DEPRECIATION RATE; MISSPECIFICATION TEST.

Genetic association study of QT interval highlights role for calcium signaling pathways in myocardial repolarization.

The QT interval, an electrocardiographic measure reflecting myocardial repolarization, is a heritable trait. QT prolongation is a risk factor for ventricular arrhythmias and sudden cardiac death (SCD) and could indicate the presence of the potentially lethal mendelian long-QT syndrome (LQTS). Using a genome-wide association and replication study in up to 100,000 individuals, we identified 35 common variant loci associated with QT interval that collectively explain ∼8-10% of QT-interval variation and highlight the importance of calcium regulation in myocardial repolarization. Rare variant analysis of 6 new QT interval-associated loci in 298 unrelated probands with LQTS identified coding variants not found in controls but of uncertain causality and therefore requiring validation. Several newly identified loci encode proteins that physically interact with other recognized repolarization proteins. Our integration of common variant association, expression and orthogonal protein-protein interaction screens provides new insights into cardiac electrophysiology and identifies new candidate genes for ventricular arrhythmias, LQTS and SCD

Countering Extremists on Social Media:Challenges for Strategic Communication and Content Moderation

Extremist exploitation of social media platforms is an important regulatory question for civil society, government, and the private sector. Extremists exploit social media for a range of reasons-from spreading hateful narratives and propaganda to financing, recruitment, and sharing operational information. Policy responses to this question fit under two headings, strategic communication and content moderation. At the center of both of these policy responses is a calculation about how best to limit audience exposure to extremist narratives and maintain the marginality of extremist views, while being conscious of rights to free expression and the appropriateness of restrictions on speech. This special issue on "Countering Extremists on Social Media: Challenges for Strategic Communication and Content Moderation" focuses on one form of strategic communication, countering violent extremism. In this editorial we discuss the background and effectiveness of this approach, and introduce five articles which develop multiple strands of research into responses and solutions to extremist exploitation of social media. We conclude by suggesting an agenda for future research on how multistakeholder initiatives to challenge extremist exploitation of social media are conceived, designed, and implemented, and the challenges these initiatives need to surmount

special issue on spatial econometrics editorial note

The present note is an intro duction to this special issue of The Review of Regional Studies, which features a selection of papers th at were originally presented a t the 7th World Conference of the Spatial Econometrics Association. The conference took place in Washington, D.C. from July 10-12, 2013. This issue contains three papers, all empirical contributions

Optimal tolerance regions for future regression vector and residual sum of squares of multiple regression model with multivariate spherically contoured errors

This paper considers multiple regression model with multivariate spherically symmetric errors to determine optimal β-expectation tolerance regions for the future regression vector (FRV) and future residual sum of squares (FRSS) by using the prediction distributions of some appropriate functions of future responses. The prediction distribution of the FRV, conditional on the observed responses, is multivariate Student-t distribution. Similarly, the prediction distribution of the FRSS is a beta distribution. The optimal β-expectation tolerance regions for the FRV and FRSS have been obtained based on the F -distribution and beta distribution, respectively. The results in this paper are applicable for multiple regression model with normal and Student-t errors

Optimal tolerance regions for some functions of multiple regression model with Student-t errors

This paper considers the multiple regression model to determine optimal beta-expectation tolerance regions for the future regression vector (FRV) and future residual sum of squares (FRSS) by using the prediction distributions of some appropriate functions of future responses. It is assumed that the errors of the regression model follow a multivariate Student-t distribution with unknown shape parameter, nu. The prediction distribution of the FRV, conditional on the observed responses, is a multivariate Student-t distribution but its shape parameter does not depend on the unknown degrees of freedom of the Student-t model. Similarly, the prediction distribution of the FRSS is a beta distribution. The optimal beta- expectation tolerance regions for the FRV and FRSS have been obtained based on the F-distribution and beta distribution respectively