中英文时间表征的对比探析

从时间的似真表征和隐喻表征对比分析可以发现汉语和英语时间表征上的异同。操汉语的人是基于客观真实时间顺序和主观想象时间顺序来构建其句子和概念的,其时间表征多为似真表征方式,英语里虽也有某种程度的似真表征,但说英语的人更多的是依据形态语法和抽象原则来表征时间关系的;时间的隐喻表征在两种语言中既有相同之处也有很大差别,这主要源于不同文化的价值观和认知方式     

交叉上市、股票发行定价与市场溢出效应

交叉上市会影响原上市地股票价格,产生所谓的溢出效应。对我国先 H股后A股和先 A股后 H股两种交叉上市路径的企业进行实证研究后发现,两种交叉上市路径在招股公告日和交叉上市日前后,对原市场均产生了明显的波动溢出效应。其中,招股公告日效应主要表现为负向效应,交叉上市日效应则表现为正向效应。进一步的实证研究发现,先H股后A股上市公司对H股所产生的溢出效应,主要是受A股市场高发行溢价率和A股股票高收益预期的影响,而先A股后 H股上市公司对A股市场产生的溢出效应,可能源于市场发达程度差异,以及由此所产生的对上市公司治理效率、信息披露、监管和投资者保护等方面的改善预期。     

文化空缺视野下中美认知模式比较研究

成功的跨文化交际不仅要了解文化差异,更要了解文化空缺现象背后认知模式的差异及其根源。中美文化中存在大量的文化空缺现象,这也是双方认知模式迥异的根源。在对文化空缺进行理论概述的基础上,对中美文化空缺现象进行对比分析,深入探讨由此带来的中美认知模式差异。在文化空缺的基础上对中美认知模式进行比较研究,有助于中美跨文化交际顺利进行,促进中美关系健康发展。     

数字化视角下的数据生产要素与资源配置重构研究: 新零售与数字化转型

借助流动性风险理论,解释数据强连接性反应的强流动性与数据泄露反应的流动性风险之间存在的张力,有效弥补“场景—风险”理论聚焦具体化规制思路而忽视上位性抽象概念逻辑涵摄的不足。其具体指向包含跨境数据控制者对数据流动样态引起的损害可能性与不确定性认知、流动性与风险度的正相关关系和风险承担模式的判断。在此基础上构建跨境数据泄露通知制度是数据泄露通知制度与数据跨境保护规则交融的规范逻辑,实现数据安全与跨境自由流动、技术扩张与规则限制、规则保护与监管制约的有效平衡价值。尝试构建针对性的跨境数据泄露通知规则、明确数据跨境泄露通知的监管联动与多边合作,助力形成体系化跨境数据泄露保护框架,有效实现数据安全保障的实践逻辑。     

基于消费者显著性偏好的双边平台转换成本和定价策略研究

针对消费者对转换成本和价格属性具有显著性偏好的实际情况,基于显著性理论对垄断双边平台企业的转换成本和定价策略问题进行了研究。研究发现：1)在显著性偏好非对称型市场上,对价格敏感的一方收取的价格最低,对高转换成本的一方收取的价格最高,而在显著性偏好对称型市场上,平台的最优价格位于非对称型市场之间,但是对价格敏感型市场收取的价格低于转换成本敏感型市场的价格。2)在高转换成本的市场,平台利润最低;低转换成本市场平台利润最高,而混合型情况的最优利润位于这两者之间。该研究结论说明平台企业应该采取措施来降低用户加入平台的转换成本,从而增加利润,这与现实的案例研究结论相吻合。     

Program Evaluation and Causal Inference With High‐Dimensional Data

In this paper, we provide efficient estimators and honest confidence bands for a variety of treatment effects including local average (LATE) and local quantile treatment effects (LQTE) in data‐rich environments. We can handle very many control variables, endogenous receipt of treatment, heterogeneous treatment effects, and function‐valued outcomes. Our framework covers the special case of exogenous receipt of treatment, either conditional on controls or unconditionally as in randomized control trials. In the latter case, our approach produces efficient estimators and honest bands for (functional) average treatment effects (ATE) and quantile treatment effects (QTE). To make informative inference possible, we assume that key reduced‐form predictive relationships are approximately sparse. This assumption allows the use of regularization and selection methods to estimate those relations, and we provide methods for post‐regularization and post‐selection inference that are uniformly valid (honest) across a wide range of models. We show that a key ingredient enabling honest inference is the use of orthogonal or doubly robust moment conditions in estimating certain reduced‐form functional parameters. We illustrate the use of the proposed methods with an application to estimating the effect of 401(k) eligibility and participation on accumulated assets. The results on program evaluation are obtained as a consequence of more general results on honest inference in a general moment‐condition framework, which arises from structural equation models in econometrics. Here, too, the crucial ingredient is the use of orthogonal moment conditions, which can be constructed from the initial moment conditions. We provide results on honest inference for (function‐valued) parameters within this general framework where any high‐quality, machine learning methods (e.g., boosted trees, deep neural networks, random forest, and their aggregated and hybrid versions) can be used to learn the nonparametric/high‐dimensional components of the model. These include a number of supporting auxiliary results that are of major independent interest: namely, we (1) prove uniform validity of a multiplier bootstrap, (2) offer a uniformly valid functional delta method, and (3) provide results for sparsity‐based estimation of regression functions for function‐valued outcomes.     

Rushes in Large Timing Games

We develop a continuum player timing game that subsumes standard wars of attrition and pre‐emption games, and introduces a new rushes phenomenon. Payoffs are continuous and single‐peaked functions of the stopping time and stopping quantile. We show that if payoffs are hump‐shaped in the quantile, then a sudden “rush” of players stops in any Nash or subgame perfect equilibrium. Fear relaxes the first mover advantage in pre‐emption games, asking that the least quantile beat the average; greed relaxes the last mover advantage in wars of attrition, asking just that the last quantile payoff exceed the average. With greed, play is inefficiently late: an accelerating war of attrition starting at optimal time, followed by a rush. With fear, play is inefficiently early: a slowing pre‐emption game, ending at the optimal time, preceded by a rush. The theory predicts the length, duration, and intensity of stopping, and the size and timing of rushes, and offers insights for many common timing games.     

Assessment of Uncertainty in High Frequency Data: The Observed Asymptotic Variance

The availability of high frequency financial data has generated a series of estimators based on intra‐day data, improving the quality of large areas of financial econometrics. However, estimating the standard error of these estimators is often challenging. The root of the problem is that traditionally, standard errors rely on estimating a theoretically derived asymptotic variance, and often this asymptotic variance involves substantially more complex quantities than the original parameter to be estimated. Standard errors are important: they are used to assess the precision of estimators in the form of confidence intervals, to create “feasible statistics” for testing, to build forecasting models based on, say, daily estimates, and also to optimize the tuning parameters. The contribution of this paper is to provide an alternative and general solution to this problem, which we call Observed Asymptotic Variance. It is a general nonparametric method for assessing asymptotic variance (AVAR). It provides consistent estimators of AVAR for a broad class of integrated parameters Θ = ∫ θ_t dt, where the spot parameter process θ can be a general semimartingale, with continuous and jump components. The observed AVAR is implemented with the help of a two‐scales method. Its construction works well in the presence of microstructure noise, and when the observation times are irregular or asynchronous in the multivariate case. The methodology is valid for a wide variety of estimators, including the standard ones for variance and covariance, and also for more complex estimators, such as, of leverage effects, high frequency betas, and semivariance.     

Using In Vitro High‐Throughput Screening Data for Predicting Benzo[k]Fluoranthene Human Health Hazards

Today there are more than 80,000 chemicals in commerce and the environment. The potential human health risks are unknown for the vast majority of these chemicals as they lack human health risk assessments, toxicity reference values, and risk screening values. We aim to use computational toxicology and quantitative high‐throughput screening (qHTS) technologies to fill these data gaps, and begin to prioritize these chemicals for additional assessment. In this pilot, we demonstrate how we were able to identify that benzo[k]fluoranthene may induce DNA damage and steatosis using qHTS data and two separate adverse outcome pathways (AOPs). We also demonstrate how bootstrap natural spline‐based meta‐regression can be used to integrate data across multiple assay replicates to generate a concentration–response curve. We used this analysis to calculate an in vitro point of departure of 0.751 μM and risk‐specific in vitro concentrations of 0.29 μM and 0.28 μM for 1:1,000 and 1:10,000 risk, respectively, for DNA damage. Based on the available evidence, and considering that only a single HSD17B4 assay is available, we have low overall confidence in the steatosis hazard identification. This case study suggests that coupling qHTS assays with AOPs and ontologies will facilitate hazard identification. Combining this with quantitative evidence integration methods, such as bootstrap meta‐regression, may allow risk assessors to identify points of departure and risk‐specific internal/in vitro concentrations. These results are sufficient to prioritize the chemicals; however, in the longer term we will need to estimate external doses for risk screening purposes, such as through margin of exposure methods.     

Incorporating Time‐Dose‐Response into Legionella Outbreak Models

A novel method was used to incorporate in vivo host–pathogen dynamics into a new robust outbreak model for legionellosis. Dose‐response and time‐dose‐response (TDR) models were generated for Legionella longbeachae exposure to mice via the intratracheal route using a maximum likelihood estimation approach. The best‐fit TDR model was then incorporated into two L. pneumophila outbreak models: an outbreak that occurred at a spa in Japan, and one that occurred in a Melbourne aquarium. The best‐fit TDR from the murine dosing study was the beta‐Poisson with exponential‐reciprocal dependency model, which had a minimized deviance of 32.9. This model was tested against other incubation distributions in the Japan outbreak, and performed consistently well, with reported deviances ranging from 32 to 35. In the case of the Melbourne outbreak, the exponential model with exponential dependency was tested against non‐time‐dependent distributions to explore the performance of the time‐dependent model with the lowest number of parameters. This model reported low minimized deviances around 8 for the Weibull, gamma, and lognormal exposure distribution cases. This work shows that the incorporation of a time factor into outbreak distributions provides models with acceptable fits that can provide insight into the in vivo dynamics of the host‐pathogen system.