基于建构主义的英语课堂教学研究

基于建构主义理论英语课堂教学应注意提倡“基于问题学习”的教学方式,鼓励学生合作与交流,强调探索与创新能力的培养。     

Unstable Inflation Targets

This paper studies long‐run inflation targets and stability in an imperfect information environment. When central banks set an inflation target that is not fully communicated, agents draw inferences about inflation from recent data and remain alert to structural change by forming expectations from a forecasting model that is estimated via discounted least squares. Inflation targets can lead agents' beliefs to depart from rational expectations through two channels. First, implementing a higher inflation target can lead to overshooting. Second, there can be nearly self‐fulfilling inflation, disinflation, or deflation that arises as an endogenous response to shocks. Policy implications for implementing a higher target without deanchoring expectations are discussed.     

团队跨边界活动对团队有效性的影响作用研究

通过文献综述,得出了团队跨边界活动对团队有效性有着倒U型的影响作用这一结论。同时,在团队学习理论和社会网络理论的基础上,对上述作用的内部机制进行研究探索,并从团队和个体两个层面分析相关的影响因素。最后,从企业管理实践的角度出发,基于研究结果提出几点思考和意见。     

Students’ conceptions of learning in the context of an accounting degree

Students' conceptions of learning (CoL) play an important role in the learning process leading to the development of generic skills. This paper investigates whether CoL of accounting students can be developed by incorporating high-level cognitive skills progressively within the accounting curriculum. First, the study explored, using phenomenography, the variation in accounting students' CoL. The findings highlighted some nuances in accountings students' CoL and that lower-order CoL were prevalent among accounting students. However, more (less) third-year students than second-year students adopted higher- (lower-) order CoL. Upon examining the learning objectives, teaching methods and assessment of all units comprising the accounting major of the host university, evidence was found that CoL are progressively developed and shaped, albeit in a limited way, based on the levels of cognitive domain emphasised within the curriculum. The findings highlight the need for accounting educators to take a whole of the programme approach to developing higher-order CoL.     

Creative tourism in Balinese rural communities

The present study extends knowledge on creative tourism in rural areas. It was unclear from previous research how features of creative tourism, such as risk of commodification, play out in rural areas of developing countries. The study built on theoretical overlaps which suggest that creative tourism may improve the potential of community-based tourism. Literature suggested that creative tourism may address three issues plaguing community-based tourism: (1) lack of financial resources could be circumvented with intangible heritage; (2) loss of cultural identity could be reversed by sparking interest for culture; (3) power relations between hosts and guests could be rebalanced by repositioning locals from servant to teacher. These theoretical overlaps were explored in the context of five Balinese villages using a microethnographic approach with participant observations and expert interviews. Findings from this study partly confirm and extend the theoretical synergies. Furthermore, a new synergy, increasing enthusiasm for intercultural exchange, and one negative interaction, intangibility as a differentiator, were found. Findings also revealed conditions for success in developing creative tourism in a community-based tourism context. In sum, we contribute the conclusion that creative tourism in rural areas is promising under certain conditions.     

Machine learning for pricing American options in high-dimensional Markovian and non-Markovian models

In this paper we propose two efficient techniques which allow one to compute the price of American basket options. In particular, we consider a basket of assets that follow a multi-dimensional Black–Scholes dynamics. The proposed techniques, called GPR Tree (GRP-Tree) and GPR Exact Integration (GPR-EI), are both based on Machine Learning, exploited together with binomial trees or with a closed form formula for integration. Moreover, these two methods solve the backward dynamic programing problem considering a Bermudan approximation of the American option. On the exercise dates, the value of the option is first computed as the maximum between the exercise value and the continuation value and then approximated by means of Gaussian Process Regression. The two methods mainly differ in the approach used to compute the continuation value: a single step of the binomial tree or integration according to the probability density of the process. Numerical results show that these two methods are accurate and reliable in handling American options on very large baskets of assets. Moreover we also consider the rough Bergomi model, which provides stochastic volatility with memory. Despite that this model is only bidimensional, the whole history of the process impacts on the price, and how to handle all this information is not obvious at all. To this aim, we present how to adapt the GPR-Tree and GPR-EI methods and we focus on pricing American options in this non-Markovian framework.     

Teaching courses in macroeconomics and monetary policy with Bloomberg analytics

In this article, the authors illustrate the use of Bloomberg for analyzing topics in macroeconomics and monetary policy in economics and finance courses. The hands-on experience that students gain from such a course has many benefits, including deeper learning and clearer understanding of data. The authors describe goals and learning objectives, then compare Bloomberg with Federal Reserve Economic Data (FRED). In addition, they provide examples of how to use Bloomberg in the classroom, describe how to have students perform sector analysis, show how Bloomberg tools are useful for analyzing monetary policy, discuss how to use Bloomberg to analyze the financial sector, and illustrate the platform’s use in a case study.     

LGB-BAG模型在P2P网贷借款者信用风险评估中的应用

立足于P2P平台,利用P2P平台个人借款人的信息建立了一套系统的信用风险评估指标体系来甄别可能违约的借款人。本文基于LightGBM（一种基于决策树的Boosting模型）和Bagging而提出一种新的LGB-BAG模型,有效结合了Boosting和Bagging的优势。结果表明,在N增大到一定程度的时候,LGB-BAG的F1均值（预测效果）要高于LightGBM和随机森林;并且LGB-BAG的F1方差也要小于其余两种模型。LGB-BAG的F1均值最高可达到0.71175,且LGB-BAG模型能够显著提高信用风险预测效率。     

The Gender Wealth Gap in Europe: Application of Machine Learning to Predict Individual-level Wealth

This article provides comparative estimates of the gender wealth gaps for 22 European countries, employing data from the Household Finance and Consumption Survey. The data on wealth are collected at the household level, while individual-level data are needed for the estimates of gender wealth gaps. We propose a novel approach using machine learning and model averaging methods to predict individual-level wealth data for multi-person households. Our results suggest that random forest performs best as the predicting tool for this exercise, outperforming elastic net and Bayesian model averaging. The estimated gender wealth gaps tend to be in favor of men, especially at the top of the wealth distribution. Men have 24 percent more wealth than women on average. We also find that a high home ownership rate is associated with a smaller country-level gender wealth gap. Our estimates suggest that the individual-level wealth inequality is on average 3 pp higher than the household-level wealth inequality in multi-member households.     

大数据能力对农业科技企业创新的影响：一个有调节的中介效应模型

目前学者们较为关注大数据能力对于企业创新的影响研究,但大多笼统探究大数据能力对企业创新的直接影响,深入探究其中介机制和调节机制的研究尚未完善,立足于农业科技企业的研究更是缺乏。本文基于动态能力理论,深入探究大数据能力对农业科技企业创新的影响机制,以双元组织学习为中介变量,以市场导向为调节变量,建立可调节的中介模型。基于181家农业科技企业样本,结果表明：大数据能力对企业创新具有显著正向影响;探索式学习和利用式学习在大数据能力和企业创新间均具有正向中介作用;市场导向增强了利用式学习在大数据能力与企业创新之间的中介作用;而对探索式学习在大数据能力与企业创新之间的中介作用未发挥显著的调节作用。