基于面板协整方法的外资与外贸关系研究

本文利用近年来最新发展起来的面板单位根及面板协整时间序列方法,对外资与中国外贸之间的关系进行了实证检验。与以往研究中普遍采用的单一截面时序分析方法相比,基于面板数据的时序方法不仅增加了数据自由度,而且综合了截面间信息,因而检验结果具有更高的势(power)值和稳健性。检验结果表明,外资与中国外贸之间存在着较为明显的互补关系。最后,本文对形成这一结果的原因作了简要说明,并指出了该结论对于政策制定的重要含义。     

State estimation of a shop floor using improved resampling rules for particle filtering

Operational inefficiencies in supply chains cost industries millions of dollars every year. Much of these inefficiencies arise due to the lack of a coherent planning and control mechanism, which requires accurate yet timely state estimation of these large-scale dynamic systems given their massive datasets. While Bayesian inferencing procedures based on particle filtering paradigm may meet these requirements in state estimation, they may end up in a situation called degeneracy, where a single particle abruptly possesses significant amount of normalized weights. Resampling rules for importance sampling prevent the sampling procedure from generating degenerated weights for particles. In this work, we propose two new resampling rules concerning minimized variance (VRR) and minimized bias (BRR). The proposed rules are derived theoretically and their performances are benchmarked against that of the minimized variance and half-width based resampling rules existing in the literature using a simulation of a semiconductor die manufacturing shop floor in terms of their resampling qualities (mean and variance of root mean square errors) and computational efficiencies, where we identify the circumstances that the proposed resampling rules become particularly useful.     

基于多分辨分析的股市与债市溢出效应研究

运用多分辨分析研究我国股市与债市溢出效应,对两市收益率序列作信号分解,分解为高频信号和低频信号,比较各层信号占原始信号的能量比发现,股市高频信号占原始信号的能量比远大于低频信号,而债市高频信号和低频信号所占能量比基本持平,从而说明了我国股市主要受短期因素影响,波动较大,而债市波动较小。高频信号的Granger因果关系检验表明,股市与债市之间存在溢出效应,但溢出效应是单向的,即只存在股市向债市的溢出效应,这与债市作为金融市场的＂避风港＂有关。     

Video-based pedestrian grouping model considering long-span space in a big hall

Pedestrian group detection is a challenging but significant issue in pedestrian flow control and public safety management. To address the issue that most conventional pedestrian grouping models (PGMs) can only identify a pedestrian group at a limited distance of less than 2 m, this study extended the pedestrian distance constraint of conventional PGMs with a reconstruction of the normal group detection criterion and development of a novel group detection criterion suitable for long-span space. To measure the movement behavior similarity with normal distance, five necessary constraints: velocity difference, moving direction offset, distance limitation, distance fluctuation, and group-keeping duration were studied quantitatively to form the criterion to detect normal groups. Meanwhile, a long-span group detection criterion was proposed with extended distance and direction consistency constraints. Therefore, this study proposed an improved PGM that considers long-span spaces (PGMLS). In the PGMLS workflow, the MMTrack algorithm was used to obtain pedestrian trajectories. A difference measurement method based on sequential pattern analysis (SPA) was adopted to analyze the velocity similarity of pedestrians. To validate the proposed grouping model, experiments based on pedestrian movement videos in the exit hall of the Shanghai Hongqiao International Airport were conducted. The results indicate that the proposed model can detect both normal and widely separated pedestrian groups, with a long span range of 2–12 m.     

Analysis of the impact of university policies on society's environmental perception

Today's society is showing great interest in achieving sustainable development in all socio-economic facets, and higher education institutions stand out as being proactive in this regard. University campuses are successfully implementing policies to curb climate change, energy and water conservation, waste recycling and green transport. In this struggle, education plays an essential role in shaping a population that is aware of the situation and willing to stop —and if possible undo— the damage caused. This study is aimed at evaluating universities' capacity to foster society's environmental perception and commitment. The analysis, which focuses on the Spanish and Italian campuses assessed by GreenMetric during the period 2018–2022, has a twofold objective: to identify the sustainable actions that have a direct impact on students' and researchers' awareness; and to analyse the efficiency of the environmental policies implemented by those responsible for these educational centres, as well as the differences between the two countries' universities. To that end, a panel data model is estimated on a sample composed of the pillars of GreenMetric, with DEA-bootstrap and the sequential Malmquist index then used to assess the efficiency of the actions undertaken. The results reveal the importance of the waste and transport policies implemented on campuses when it comes to the arduous task of kindling society's interest in the environment. Furthermore, both countries show increasing engagement, with productivity improvements of over 36% in the case of Italy.     

自相关检验方法的检验功效研究

研究目标:探究DW检验和LM检验的检验功效及其渐近性。研究方法:运用蒙特卡罗模拟实验方法结合相关影响因素对两种检验方法进行分析与比较。影响因素包括样本容量、解释变量的随机性及自相关性、随机误差项的自相关程度以及分布形态。研究发现:DW和LM检验功效与样本容量和随机误差项的自相关程度正相关,与解释变量的自相关程度负相关;解释变量的随机性对DW和LM检验功效无显著影响;误差项的几种常见分布形态的变化对DW和LM检验功效的影响可以忽略;在误差项存在一阶自相关的情况下,DW检验效果优于LM检验效果。研究创新:以DW检验和LM检验的假设条件为出发点,探究比较不同条件下自相关检验方法的检验功效。研究价值:在实证研究背景下为有效地选择自相关检验方法提供借鉴参考。     

基于经营业绩观研究我国上市公司财务困境成本计量问题——以制造业为例

选取沪深两市1998-2012年被ST的74家制造业上市公司(A股)样本,以主营业务净利润率为指标结合单样本T检验从经营业绩观视角计量制造业上市公司的财务困境问题,结果表明我国制造业上市公司陷入财务困境后需要承受明显的财务困境成本。     

Creating and detecting fake reviews of online products

Customers increasingly rely on reviews for product information. However, the usefulness of online reviews is impeded by fake reviews that give an untruthful picture of product quality. Therefore, detection of fake reviews is needed. Unfortunately, so far, automatic detection has only had partial success in this challenging task. In this research, we address the creation and detection of fake reviews. First, we experiment with two language models, ULMFiT and GPT-2, to generate fake product reviews based on an Amazon e-commerce dataset. Using the better model, GPT-2, we create a dataset for a classification task of fake review detection. We show that a machine classifier can accomplish this goal near-perfectly, whereas human raters exhibit significantly lower accuracy and agreement than the tested algorithms. The model was also effective on detected human generated fake reviews. The results imply that, while fake review detection is challenging for humans, “machines can fight machines” in the task of detecting fake reviews. Our findings have implications for consumer protection, defense of firms from unfair competition, and responsibility of review platforms.     

Understanding the sequential interdependence of mobile app adoption within and across categories

This research examines the interdependencies in users’ sequential app adoptions within and across diverse app categories. We employ a Zero-inflated Negative Binomial (ZINB) model to analyze a unique, granular, and individual-level mobile app adoption dataset, revealing three main findings. First, users’ app adoption decisions are highly history-dependent and category-specific in a nonlinear fashion. Early adoption can enhance subsequent downloads within the same category for app categories with high needs evolvement and horizontal differentiation (e.g., Game and Education apps). However, it may crowd out subsequent downloads in other categories with low needs evolvement and horizontal differentiation (e.g., Communication and Social media apps). Second, these effects are further moderated by users’ individual characteristics such as app usage tenure and phone price. Third, there exist nontrivial app adoption spillovers across app categories. For example, users’ adoptions of apps with relatively high hedonic values (e.g., Game and Music apps) can suppress their subsequent need for apps with relatively high utilitarian values (e.g., Education and Online banking apps), and vice versa. Together, these results offer novel managerial implications for app developers and platforms to promote apps in different categories based on users’ adoption histories.     

Model uncertainty,economic development,and welfare costs of business cycles

Some researchers argue that the welfare gains from eliminating consumption fluctuations for the United States are not small once model uncertainty is taken into account. This paper presents new evidence on the welfare gains from eliminating model uncertainty using a data set from a broad range of countries. It quantifies exactly the effect of model uncertainty on the welfare gains using an analytical formula. The results indicate that most countries derive much larger gains from the reduction of model uncertainty compared with the United States. Countries at higher stages of economic development tend to have lower welfare gains because their gains from eliminating model uncertainty become smaller. This relationship does not depend on country size or trade openness.