Catching up with Harvard: results from regression analysis of world universities league tables

This paper uses regression analysis to test if the universities performing less well according to Shanghai Jiao Tong University’s world universities league tables are able to catch up with the top performers, and to identify national and institutional factors that could affect this catching up process. We have constructed a dataset of 461 universities across 41 countries. We found consistent evidence of a moderate degree of catching up, especially amongst non-US universities. Larger universities as well as universities located in English speaking countries not only perform better on average, but also catch up more over 2003–2009. Universities located in lower income countries are also catching up more. The performance of private universities, as compared to that of public universities, varies substantially between the US and the other countries.     

中国创业板市场收益率波动性的贝叶斯分析

采用随机波动（SV）模型,实证研究我国创业板市场收益率的波动性.通过基于Gibbs抽样的贝叶斯分析方法,较好地估计了模型参数.基于创业板市场数据的实证结果表明,带杠杆效应的SV-L模型相比基本的SV-N模型能更好地描述股票市场收益率的波动性.     

On the interplay between normalisation,bias, and performance of paper impact metrics

We evaluate article-level metrics along two dimensions. Firstly, we analyse metrics’ ranking bias in terms of fields and time. Secondly, we evaluate their performance based on test data that consists of (1) papers that have won high-impact awards and (2) papers that have won prizes for outstanding quality. We consider different citation impact indicators and indirect ranking algorithms in combination with various normalisation approaches (mean-based, percentile-based, co-citation-based, and post hoc rescaling). We execute all experiments on two publication databases which use different field categorisation schemes (author-chosen concept categories and categories based on papers’ semantic information).In terms of bias, we find that citation counts are always less time biased but always more field biased compared to PageRank. Furthermore, rescaling paper scores by a constant number of similarly aged papers reduces time bias more effectively compared to normalising by calendar years. We also find that percentile citation scores are less field and time biased than mean-normalised citation counts.In terms of performance, we find that time-normalised metrics identify high-impact papers better shortly after their publication compared to their non-normalised variants. However, after 7 to 10 years, the non-normalised metrics perform better. A similar trend exists for the set of high-quality papers where these performance cross-over points occur after 5 to 10 years.Lastly, we also find that personalising PageRank with papers’ citation counts reduces time bias but increases field bias. Similarly, using papers’ associated journal impact factors to personalise PageRank increases its field bias. In terms of performance, PageRank should always be personalised with papers’ citation counts and time-rescaled for citation windows smaller than 7 to 10 years.     

基于Copula函数的中美大豆期货波动溢出效应研究

通过构建时变二元正态Copula模型,分析中美大豆期货市场之间的相关关系,并对相关关系做了变结构点的诊断。通过实证分析发现,中美大豆期货市场之间在金融危机前后,相关关系发生了显著的变化,存在明显的波动溢出效应。研究波动溢出效应对准确了解我国大豆期货市场的风险具有重要意义。     

Using the structure of overlap between search results to rank retrieval systems without relevance judgments

This paper addresses the problem of how to rank retrieval systems without the need for human relevance judgments, which are very resource intensive to obtain. Using TREC 3, 6, 7 and 8 data, it is shown how the overlap structure between the search results of multiple systems can be used to infer relative performance differences. In particular, the overlap structures for random groupings of five systems are computed, so that each system is selected an equal number of times. It is shown that the average percentage of a system’s documents that are only found by it and no other systems is strongly and negatively correlated with its retrieval performance effectiveness, such as its mean average precision or precision at 1000. The presented method uses the degree of consensus or agreement a retrieval system can generate to infer its quality. This paper also addresses the question of how many documents in a ranked list need to be examined to be able to rank the systems. It is shown that the overlap structure of the top 50 documents can be used to rank the systems, often producing the best results. The presented method significantly improves upon previous attempts to rank retrieval systems without the need for human relevance judgments. This “structure of overlap” method can be of value to communities that need to identify the best experts or rank them, but do not have the resources to evaluate the experts’ recommendations, since it does not require knowledge about the domain being searched or the information being requested.     

基于月度数据对中国出口贸易的实证分析

本文选取1994年1月到2008年8月的月度数据,在考虑了中国加入WTO和2005年7月汇改的结构性影响之后,运用Johansen协整检验以及误差修正模型,对我国出口与外国实际收入、中国相对出口价格、汇率的波动性之间的关系进行了实证分析.Johansen协整检验的结果表明:我国出口与外国实际收入、中国相对出口价格具有长期的均衡关系而且中国出口对主要贸易伙伴国实际收入的弹性(为正数)很高,中国出口的价格弹性(为负数)也较高;误差修正模型的结果显示:较大的汇率波动性能够导致出口减少.这都突出反映了我国经济一直以来过度依赖外需推动,内需严重不足的问题以及我国出口产业本身的深层次问题.     

融资融券是否平抑了中国股市的波动性？

具备卖空和杠杆特性的融资融券交易机制在我国证券市场运行已逾五年,但其对股市波动性影响几何始终存在分歧。本文采用GARCH回归、VAR模型、脉冲响应、方差分解等计量方法对沪深300股票指数的波动性展开实证研究,结果表明：融资融券总体上平抑了我国股市的波动性,且随着标的股票的扩容逐渐加强;但在趋势性较为明确的单一行情中,融资融券却进一步加大了股市的波动性;从分效应来看,融资交易和融券交易也都抑制了股市波动,但融券的作用幅度小于融资;与股指期货的比较研究则发现股指期货加大了现货市场的波动,但作用幅度小于融资融券。     

国内部分师范类院校学科建设水平现状研究——基于2017年全国学科评估的数据

以2017 年全国学科评估的数据为基础,对我国师范类院校学科评估的情况进行多角度的排名分析,并选取其中位居前列的院校作进一步研究,划分为若干个集团,对处于不同集团师范院校的学科实力进行比较,包括比较各自获A+ 学科的情况,及在人文类学科、艺术类学科、理学门类、工学门类、医学门类、管理学门类中的表现等等,进一步从得分、高校属性、高校所在的地域探讨了这些院校学科建设水平的现状与格局。对获得不同层次成绩的学科,如在学科评估中获得较好成绩（如获得A 类等级）的学科、仍有较大进步空间的学科特别是弱势学科、仍未参评的学科,提出了不同的学科建设方面的建议。最后特别针对大部分地方师范类院校,提出“教育学科+ 特色学科”的学科建设模式。     

Dealing with textual noise for robust and effective BERT re-ranking

The pre-trained language models (PLMs), such as BERT, have been successfully employed in two-phases ranking pipeline for information retrieval (IR). Meanwhile, recent studies have reported that BERT model is vulnerable to imperceptible textual perturbations on quite a few natural language processing (NLP) tasks. As for IR tasks, current established BERT re-ranker is mainly trained on large-scale and relatively clean dataset, such as MS MARCO, but actually noisy text is more common in real-world scenarios, such as web search. In addition, the impact of within-document textual noises (perturbations) on retrieval effectiveness remains to be investigated, especially on the ranking quality of BERT re-ranker, considering its contextualized nature. To mitigate this gap, we carry out exploratory experiments on the MS MARCO dataset in this work to examine whether BERT re-ranker can still perform well when ranking text with noise. Unfortunately, we observe non-negligible effectiveness degradation of BERT re-ranker over a total of ten different types of synthetic within-document textual noise. Furthermore, to address the effectiveness losses over textual noise, we propose a novel noise-tolerant model, De-Ranker, which is learned by minimizing the distance between the noisy text and its original clean version. Our evaluation on the MS MARCO and TREC 2019–2020 DL datasets demonstrates that De-Ranker can deal with synthetic textual noise more effectively, with 3%–4% performance improvement over vanilla BERT re-ranker. Meanwhile, extensive zero-shot transfer experiments on a total of 18 widely-used IR datasets show that De-Ranker can not only tackle natural noise in real-world text, but also achieve 1.32% improvement on average in terms of cross-domain generalization ability on the BEIR benchmark.     

国际大宗商品价格与我国经济周期关联性研究

根据2000年1月—2011年11月的月度数据,采用转折点分析、相关系数分析和时间序列分析(MS-VAR模型)研究国际大宗商品价格与我国经济周期的关联性,结果表明:国际大宗商品价格与我国经济周期存在较强的正相关性,且国际大宗商品价格与我国工业增加值、CPI之间存在状态转移特征。国际大宗商品价格变动领先于我国工业增加值同比增长而滞后于我国CPI同比增长,三者的波动特征极为相似,平均周期长度也较为接近,且存在格兰杰因果关系;我国经济周期的扩张和收缩是引起国际大宗商品价格波动的一个重要原因,而国际大宗商品价格的波动则会加剧我国经济周期的扩张和收缩。我国应积极培育新兴进出口市场,建立大宗商品的国家储备制度及其信息系统和监测预警机制,以分散和防范国际大宗商品价格风险。