Testing the asymmetric effects of exchange rate pass‐through in BRICS countries: Does the state of the economy matter?

This paper investigates not only the question of whether there is exchange rate pass‐through (ERPT) but also the extent to which the pass‐through is asymmetric or state‐dependent in the BRICS countries. Using monthly data from 1999:M1 to 2019:M12 and non‐linear smooth transition vector autoregressive (STVAR) model, our results provide evidence of period‐specific ERPT between the upper and lower regime periods, governed by the selected transition variables. The results further suggest that the pass‐through of exchange rate is higher when the economy is experiencing large appreciations and expansions as well as large depreciations and recessions. Theimplication for these findings is that ERPT is strongly affected by the state of the economy.     

市域(郊)铁路车辆选型方案探讨

随着城镇化进程的加快和城市经济辐射能力的增强,以特大城市中心城为核心的都市圈逐渐形成,都市圈内的经济联系更加紧密,都市圈核心区与外围的出行需求迅速增长,市域(郊)铁路的概念被引进,选择符合市域(郊)铁路特点的车辆类型是设计的重点。     

A truly global crisis? Evidence from contagion dependence across international REIT markets

This study examines the global nature of the recent crisis under bivariate Markov-switching models for pre- and post-crisis periods using the breakpoint of August 9, 2007. It quantifies international synchronization of boom-bust regime switches to investigate contagion-type dynamic comovements of Real Estate Investment Trusts (REITs). Global REIT markets display persistent bust regimes from September 2008 to May 2009, whereas the regime-switching patterns are not significant in the pre-crisis period. The results provide new evidence for global REIT contagion phenomena and suggest greater difficulties in diversifying risks across global REIT markets during the post-crisis period.     

海南城乡居民消费水平差异性分析和预测

从1985-2018年海南城乡居民收入、消费数据出发，分别建立消费关于收入、消费差距关于收入差距的自回归模型，结果表明农村居民当期消费更易受近期收入和消费水平的影响，具有不稳定性。再利用GM(1,1)模型预测城乡居民收入差距，结合自回归模型，完成未来10年海南城乡消费差距的预测：收入差距和消费差距将呈直线递增趋势，收入差距的增幅高于消费差距增幅。缩小海南城乡居民消费差距的根本途径在于缩小收入差距，应因地制宜，切实提高农民收入水平。     

A cost-effectiveness analysis of revaccination and catch-up strategies with the 23-valent pneumococcal polysaccharide vaccine (PPV23) in older adults in Japan

Objective: In Japan, the National Immunization Program (NIP) includes PPV23 as the primary vaccination for adults and catch-up cohorts. The Japanese Association for Infectious Diseases recommends revaccination for older adults who received primary vaccination ≥5 years earlier. The cost-effectiveness of adding revaccination and/or continuing catch-up vaccination in the NIP was evaluated from the public payer perspective in Japan.

Methods: The Markov model included five health states: no pneumococcal disease, invasive pneumococcal diseases (IPD), non-bacteremic pneumococcal pneumonia (NBPP), post-meningitis sequelae, and death. Cohorts of adults aged 65–95 were followed until age 100 or death: 2014 cohort (aged 65–95, vaccinated: 2014); 2019 cohort (aged 65: 2019); and 2019 catch-up cohort (aged 70–100: 2019, unvaccinated: 2014). Strategies included: (1) vaccinate 2014 and 2019 cohorts; (2) vaccinate 2014 and 2019 cohorts and revaccinate both; (3) strategy 1 and vaccinate 2019 catch-up cohort; (4) strategy 2 and vaccinate 2019 catch-up cohort; and (5) strategy 4 and revaccinate 2019 catch-up cohort. Parameters were retrieved from global and Japanese sources, costs and QALYs discounted at 2%, and incremental cost-effectiveness ratios (ICERs) estimated.

Results: Strategy 1 had the highest number of IPD and NBPP cases, and strategy 5 the lowest. Strategies 3–5 dominated strategy 1 and strategy 2 was cost-effective compared to strategy 1 (ICER: ¥1,622,153 per QALY gained). At a willingness-to-pay threshold of ¥5 million per QALY gained, strategy 2 was cost-effective and strategies 3–5 were cost-saving compared to strategy 1.

Conclusions: Strategies including revaccination, catch-up, or both were cost-effective or cost-saving in comparison to no revaccination and no catch-up. Results can inform future vaccine policies and programs in Japan.     

Effects of individual resources and team-member exchange on service quality

While workplace resources are generally viewed as positively affecting performance, some studies indicate that not all resources increase performance. This study addresses the controversial disparate effects of workplace resources on performance by exploring functionally classified workplace resources in self-managing service teams and their relative impacts on team service quality. Considering membership dynamics and consequences between members, a field experiment was conducted in a cafeteria, with data collected through a working diary for workplace resources and a customer survey to evaluate service quality. Results suggest that efficacy-resources in self-managing teams should be controlled with caution as they may adversely affect team service quality. Supporting the person–situation interactionism perspective, workplace resources interact dynamically with individual employees and with situations. Esteem-resources were found to increase team service quality, while team-member exchange not only improved team service quality but also moderated the impact of esteem-resources on team service quality.     

The impact of influencers' multi-SNS use on followers’ behavioral intentions: An integration of cue consistency theory and social identity theory

The considerable marketing potential brought by the rapid growth of social media has been extensively examined in the literature, particularly in terms of the impact of influencers' recommendations on their followers' decisions. However, little attention has been given to how the individual influencers' use of multiple social networking sites affects their followers' behavioral intentions. Hence, this academic gap warrants further exploration. Drawing on cue consistency theory, social identity theory, and stimulus-organism-response framework, this research proposes three research questions and constructs an integrated research model to explore how the influencers' cue consistency affects the social identification of their followers, subsequently influencing their behavioral intentions. A structural equation modeling technique is performed to analyze the data of 510 valid respondents. The findings show that influencers' consistent cues (i.e., information and image consistency) across social media have positive impacts on followers' cognitive and affective identification toward the influencers. Furthermore, the determinants for followers' purchase intention and electronic word-of-mouth intention are affirmed to serve as both cognitive and affective identification. Especially, the results indicate that the relationships between followers’ social identifications and behavioral intentions are moderated by the type of influencer (i.e., lifestyle vs. review). Accordingly, the results indicate how the use of multiple social networking sites by influencers impacts the behavioral intentions of their followers. The findings offer new insights into influencer marketing and provide important lessons for marketers.     

Plausible versus implausible tensile price claim: Selective accessibility model approach

Retailers frequently use exaggerated price discount advertisements with a tensile price claim (TPC; e.g., “Save up to 70%”) to attract consumers because they expect that once consumers enter a store, they will purchase low‐ or medium‐discounted products. Drawing on the selective accessibility model, this study investigated the way in which an implausibly high maximum level of savings stated in a TPC influences consumers’ expected price discount (EPD) and perceptions of actual price discounts across different types of TPCs (i.e., TPC stating a maximum level and TPC stating a range of savings). This study also investigated two situations in which consumers have previous knowledge of a product’s price discount versus when they have less or no knowledge of the discount. For both conditions, a single‐anchor TPC (i.e., “Save up to Y%”) that stated an implausible maximum level of savings led to a higher EPD and lower perceptions of the deal (i.e., perceived savings, price fairness, and perceived value) with respect to the actual price discount than did a TPC with a plausible maximum level of savings. In contrast, when the TPC stated two anchors (i.e., “Save X–Y%”) and consumers had knowledge of the price discount, their EPDs assimilated only toward the plausible anchor (X), and ignored the implausibly high maximum price discount (Y), resulting in a lower EPD and higher perceptions of the deal of the actual price discounts than a TPC that stated a plausibly high maximum level of savings. In contrast, when consumers had no knowledge of the price discount, their EPDs only adjusted toward the more plausible anchor (X), regardless of whether they perceived the maximum anchor as plausible or implausible. Thus, there was no difference in consumers’ perceptions of “Save X–Y%” between implausibly and plausibly high Y%.     

LT码新型度分布及其在电力线通信中的应用

Luby变换(Luby Transform,LT)码作为信道编码应用于电力线通信(Power Line Communication,PLC)，可实现电信号的可靠传输。度分布对LT码编译码性能的影响至关重要。为了得到更优的度分布，首先调整二进制指数分布(Binary Exponential Distribution,BED)中的度数比例，获得一种译码性能更优的改进的二进制指数分布(Improved BED,IBED)。然后，根据IBED在冗余度较小时译码成功率高，而冗余度增大后鲁棒孤子分布(Robust Soliton Distribution,RSD)的译码性能表现更佳的特点，通过求和归一化的方式将IBED与RSD两种度分布的优势进行有机结合，提出一种新型二进制鲁棒孤子分布(Binary RSD,BRSD)。仿真结果表明，与其他方法及传统的RSD相比，采用新度分布进行LT编码，可明显降低译码开销，并节约编译码耗时。将新型度分布应用于基于LT码的PLC系统中，能有效地抑制PLC信道中各种噪声对电信号的干扰，并提高通信效率。     

A multivariate distance nonlinear causality test based on partial distance correlation: a machine learning application to energy futures

This paper proposes a multivariate distance nonlinear causality test (MDNC) using the partial distance correlation in a time series framework. Partial distance correlation as an extension of the Brownian distance correlation calculates the distance correlation between random vectors X and Y controlling for a random vector Z. Our test can detect nonlinear lagged relationships between time series, and when integrated with machine learning methods it can improve the forecasting power. We apply our method as a feature selection procedure and combine it with the support vector machine and random forests algorithms to study the forecast of the main energy financial time series (oil, coal, and natural gas futures). It shows substantial improvement in forecasting the fuel energy time series in comparison to the classical Granger causality method in time series.