Quotas and equilibrium relative prices in the trade restricting country

This paper is concerned with the effects of international trade restriction through quotas. It is shown that a casual relationship exists between the fraction of licences distributed domestically, the terms of trade, and the domestic relative price of the importable.     

Combining qualitative, quantitative and structural dimensions in a longitudinal perspective. The case of network influence

Some research questions involve several dimensions. The most rewarding approach may be switching (under control) between the diverse methods and databases involved. Having recourse to different dimensions that form a system produces wider results but also opens up new questions. A survey on the evolutions of social networks and life transitions of young people draws an original design including contextual names generators, questionnaires and interviews in a longitudinal perspective. Young people interviewed every 3 years on 4 survey waves produced a total of 287 interviews and networks and a global amount of 10,804 relationships informed. This article aims to highlight this specific potential of integrating qualitative, qantitative and structural dimensions in an iterative process of data construction and analysis. The methodological argument is empirically illustrated with a focus on the thematic of influence of the network upon life orientations. It shows influence comes mainly from strong central ties but also from some peripheral isolated mates. The structural evolution reveals a general trend of dissociation of their ties the ones from the others when people are entering adult life, which makes influence from the network become more and more diversified.     

Transatlantic innovation infrastructure networks: public-private, EU–US R&D partnerships

Collaborative, team-based research is now the most significant mode of activity in the global scientific community. Anecdotal and statistical evidence shows moreover that collaboration in scientific research is increasingly global in nature. That is, the groups of researchers who are involved in scientific progress often span one or more nations in origin, location and/or sponsorship. Another significant trend in recent cases of scientific collaboration is the increase in cross-sectoral cooperation, where researchers in a group are employed by government, private industry, and/or academic and other non-profit institutions.
In this paper, we review the scale, scope and intensity of cross-national, cross-sectoral research collaboration through the analysis of historical data on co-authorship of scientific publications. The first part of the paper reviews existing literature on the analysis of co-authorship data, and discusses the limitations of this form of analysis and typical strategies to mitigate those limitations. The second part of the paper describes a preliminary study of cross-national, cross-sectoral scientific collaborations covering the years 1988 through 1997, where we examined the scale (volume of co-authored papers), intensity (co-authored papers versus other kinds of co-authorship), and scope (patterns in co-authorship) for cross-national, cross-sectoral collaborations. The conclusion of the paper discusses significant trends and patterns derived from this study, and their implications for further research into these types of collaborations.     

Subsampling the distribution of diverging statistics with applications to finance

In this paper we propose a subsampling estimator for the distribution of statistics diverging at either known or unknown rates when the underlying time series is strictly stationary and strong mixing. Based on our results we provide a detailed discussion of how to estimate extreme order statistics with dependent data and present two applications to assessing financial market risk. Our method performs well in estimating Value at Risk and provides a superior alternative to Hill's estimator in operationalizing Safety First portfolio selection.     

HOW DO RATING AGENCIES’ DECISIONS IMPACT STOCK MARKETS? A META‐ANALYSIS

The purpose of this study is to examine how credit rating agencies’ decisions impact the stock market using a systematic and quantitative review of existing empirical studies. Specifically, we employ a meta‐regression analysis (MRA) to investigate the extent and nature of the effect of rating agencies’ decisions on the stock market. We survey 62 studies published between 1978 and 2015. Our first finding is that the cumulative average abnormal returns calculated from this empirical literature are affected by publication bias. After controlling for publication bias, the main findings of our meta‐analysis indicate that negative rating decisions cause statistically significant negative abnormal returns. This evidence suggests an informational effect. Our results also indicate that positive rating decisions do not have a significant effect. Finally, the MRA results reveal the importance of several factors related to primary study design, as well as to the nature of the data.