Perseverance and suspense in tug-of-war

We study a tug-of-war game between two players using the lottery contest success function (CSF) and a quadratic cost (of effort) function. We construct a pure strategy symmetric Markov perfect equilibrium of this game, show that it is unique, and provide closed-form solutions for equilibrium strategies and values. In stark contrast to a model of tug-of-war with an all-pay auction CSF, players exert positive efforts until the very last battle in this equilibrium. We deliver a set of empirically appealing results on effort dynamics.     

基于联合分析法的城市住区绿地雨水设施 居民喜好度研究

城市化进程带来如城市内涝等诸多环境问题，使得 海绵城市和低影响开发成为热点。城市住区绿地作为城市绿地 的一个重要类型，既是住区居民重要的景观游憩场地，也是海 绵城市低影响开发的重要海绵体，是兼具径流绩效和景观绩效 等综合绩效的复合设施。如何让雨水设施在发挥径流绩效的同 时兼具满足居民需求的景观绩效，是目前相关研究的难点和热 点。居民喜好度的研究是住区绿地雨水设施景观绩效优化提升 的重要方法之一。通过联合分析法，遴选雨水收集、雨水转 输和雨水调蓄3个环节的源头减排雨水设施，模拟江南住区中 心绿地雨水设施场景收集居民评价，进行不同居民群体的设 施喜好度效用值相关性统计和住区绿地不同类型雨水设施效 用值及相对重要性分析。并通过走访、文献查阅解析所得数 据，为住区绿地雨水设施景观绩效提升和低影响开发设计提 供启示和借鉴。     

汇水系统绿地雨洪调蓄研究——以武汉港西汇水系统为例

绿地有一定的雨洪调蓄功能，雨洪状况下，如何在汇水系统尺度利用和保护绿地亟待研究。选取95处位于武汉港西汇水系统的地块，调研地块性质，利用ArcGIS提取不透水比例及平均坡度，并进行SWMM水文模型演算。通过实验性模拟结果的对比，得出汇水系统的绿地雨洪调蓄效率(λ＝0.13m3/m2)、汇水系统(g＝36%)和各类用地(g＝18%～47%)基于雨水调蓄理念下的最低绿地率，使汇水系统水文状况达到海绵城市建设标准。讨论了不同重现期下汇水系统的绿地雨洪调蓄效能，旨在完善绿地雨洪调蓄的研究方法、指标体系及利用与保护策略，为相关研究与实践提供借鉴。     

土壤污染防治法背景下的城乡绿地系统建设：趋势与对策

《中华人民共和国土壤污染防治法》的颁布标志着中国将形成完善的土壤污染防治法律体系，建立从污染土壤调查、修复、风险防控、监测到场地再利用，从环境标准到规划管理的全方位制度。生态文明制度建设的宏观背景，再加上中国土壤污染严重、规模大、修复难、成本高、周期长等特征，将导致大量污染场地转变为绿地或生态用地，最终对绿地系统的数量与规模、布局与功能、规划与管理、环境质量标准等多个维度产生实质性影响。分析这一趋势，对其影响提出预判，最终提出对策建议。     

The role of public space in constructing experience capital: A longitudinal analysis in the hotel context

Public space plays a primary role in shaping customers' hospitality experiences. Yet how public space conditions customers' experiential outcomes in accumulating capital for hospitality organizations remains underexplored. Inspired by the theory of psychological ownership, this research presents an in-depth analysis of the impacts of customers' public space experiences on their experiential outcomes using a longitudinal hotel industry dataset merging information from customer surveys, property performance, and surrounding accessibility insights. Findings revealed the positive effects of customers’ public space experiences on their overall service experiences, the perceived value of the experience, revisit intentions, and recommendation intentions. Moreover, hotel class, other customers, and surrounding accessibility were empirically verified as moderators conditioning the positive impact of public space. These findings offer valuable implications for theory and practice that are worthy of further exploration.     

Debt Overhang and Deleveraging in the US Household Sector: Gauging the Impact on Consumption

Using a novel data set for the U.S. states, this paper examines whether household debt and the protracted debt deleveraging help explain the dismal performance of U.S. consumption since 2007 in the aftermath of the housing bubble. By separating the concepts of deleveraging and debt overhang—a flow and a stock effect—we find that excessive indebtedness exerted a meaningful drag on consumption over and beyond wealth and income effects. The overall effect, however, is modest—‐around one sixth of the slowdown in consumption between 2000–06 and 2007–12—and mostly driven by states with particularly large imbalances in their household sector. This might be indicative of non‐linearities, whereby indebtedness begins to bite only when misalignments from sustainable debt dynamics become excessive.     

Predictive regressions under asymmetric loss: Factor augmentation and model selection

This paper discusses the specifics of forecasting using factor-augmented predictive regressions under general loss functions. In line with the literature, we employ principal component analysis to extract factors from the set of predictors. In addition, we also extract information on the volatility of the series to be predicted, since the volatility is forecast-relevant under non-quadratic loss functions. We ensure asymptotic unbiasedness of the forecasts under the relevant loss by estimating the predictive regression through the minimization of the in-sample average loss. Finally, we select the most promising predictors for the series to be forecast by employing an information criterion that is tailored to the relevant loss. Using a large monthly data set for the US economy, we assess the proposed adjustments in a pseudo out-of-sample forecasting exercise for various variables. As expected, the use of estimation under the relevant loss is found to be effective. Using an additional volatility proxy as the predictor and conducting model selection that is tailored to the relevant loss function enhances the forecast performance significantly.     

一种级联两箱通用数字预失真器及其辨识算法

提出了一种由单形规范线性分段(SCPWL)函数与记忆多项式级联的数字预失真器，并给出了复数域两步最小二乘参数辨识算法。不同于以往一种预失真器适用一种功放模型的情况，所提的预失真算法利用SCPWL函数的分段特性以及记忆多项式的非线性记忆特性，在完成参数辨识的同时自动地调整结构，可适用于传统以及强非线性新型功放模型的线性化补偿。将所提预失真器分别应用于传统记忆多项式、两箱模型以及新型包络跟踪功放。经过计算机仿真，功放输出的幅频特性和频谱曲线表明所提预失真器能够有效地补偿多种功放的非线性特性。算法仿真比较结果也表明，针对包络跟踪功放，所提复数两步最小二乘算法的邻道泄漏比(ACLR)可改善约35 dB，性能优于最小均方(LMS)类算法约30 dB。     

How justified is abandoning money in the conduct of monetary policy in Australia on the grounds of instability in the money‐demand function?

The role of money in the design and conduct of monetary policy has reemerged as an important issue in both advanced and developing economies, especially since the 2007 global financial crisis. A growing body of recent literature suggests that the causal relationship between money supply growth and inflation remains intact across countries and over time and that this relation is not conditional on the stability of the money‐demand function or whether money is endogenous or exogenous. Moreover, critical for a rule‐based monetary policy is the presence of a long‐run stable money‐demand function, rather than a short‐run money‐demand model that may exhibit instability for many reasons, including problems with estimating a money‐demand model with high‐frequency data. Provided that a stable money‐demand function exists, it could be useful to establish long‐run equilibrium relations among money, output, prices, and exchange rates, as the classical monetary theory suggests. Within this analytical framework, this paper addresses the question of whether money has any role in the conduct of monetary policy in Australia. The conventional wisdom is that the money‐demand function in Australia has been unstable since the mid‐1980s due to financial deregulation and reforms; this led to a change in the strategy of monetary policy for price stability in the form of inflation targeting that ignores money insofar as inflation and its control are concerned. This paper reports empirical findings for Australia, obtained from a longer quarterly data series over the period 1960Q1–2015Q1, which suggest that instability in the narrow‐money‐demand function in Australia was primarily due to the exclusion of variables which have become important in the deregulated environment since the 1980s. These findings are confirmed by an expanded form of the narrow‐money‐demand function that was found stable over the past two decades, although it experienced multiple structural breaks over the study period. The paper draws the conclusion that abandoning the monetary aggregate as an instrument of monetary policy in Australia, under a rule‐based monetary policy such as inflation targeting, cannot be justified by instability in the money‐demand function or even by lack of a causal link between money supply growth and inflation.     

Big data driven cycle time parallel prediction for production planning in wafer manufacturing

Cycle time forecasting (CTF) is one of the most crucial issues for production planning to keep high delivery reliability in semiconductor wafer fabrication systems (SWFS). This paper proposes a novel data-intensive cycle time (CT) prediction system with parallel computing to rapidly forecast the CT of wafer lots with large datasets. First, a density peak based radial basis function network (DP-RBFN) is designed to forecast the CT with the diverse and agglomerative CT data. Second, the network learning method based on a clustering technique is proposed to determine the density peak. Third, a parallel computing approach for network training is proposed in order to speed up the training process with large scaled CT data. Finally, an experiment with respect to SWFS is presented, which demonstrates that the proposed CTF system can not only speed up the training process of the model but also outperform the radial basis function network, the back-propagation-network and multivariate regression methodology based CTF methods in terms of the mean absolute deviation and standard deviation.