Should sequels differ from original movies in pre-launch advertising schedule? Lessons from consumers' online search activity

Sequels have become a profitable strategy in the U.S. motion picture industry because of their strong name recognition. However, while the established positioning of a sequel may help insulate it from competing firms' advertising messages, its familiarity may cause moviegoers to be more easily satiated with advertising from the sequel. Therefore, this study examines how sequels differ from original concept movies in terms of their ad effectiveness. We focus our analysis on pre-launch periods, given these periods' importance in shaping the financial outcomes of motion pictures. We consider the weekly online search volume of a movie as a measure of consumer interest in it, and thus as an intermediate response to pre-launch advertising. We then develop a model that assumes ad effectiveness can decline, due to copy and repetition wearout, and increase, due to forgetting, over time. We find that copy wearout is greater for original movies, while repetition wearout and forgetting are greater for sequels. These findings suggest that sequels should allocate more in early pre-launch periods and less immediately before release, relative to originals, to maximize pre-launch consumer interest.     

Information Flows across Markets: Evidence from China–Backed Stocks Dual–Listed in Hong Kong and New York

Using a bivariate generalized autoregressive conditional heteroskedasticity (GARCH) model, we examine patterns of information flows for China–backed stocks that are cross–listed on exchanges in Hong Kong and New York. Results analyzing the dual–listed stocks indicate significant mutual feedback of information between domestic (Hong Kong) and offshore (New York) markets in terms of pricing and volatility. Stocks listed on the domestic market appear to play a more significant role of information transmission in the pricing process, whereas stocks listed on the offshore market play a bigger role in volatility spillover.     

Modelling soccer matches using bivariate discrete distributions with general dependence structure

In this paper copulas are used to generate bivariate discrete distributions. These distributions are fitted to soccer data from the English Premier League. An interesting aspect of these data is that the primary variable of interest, the discrete pair shots-for and shots-against, exhibits negative dependence; thus, in particular, we apply bivariate Poisson-related distributions that allow such dependence. The paper focuses on Archimedian copulas, for which the dependence structure is fully determined by a one-dimensional projection that is invariant under marginal transformations. Diagnostic plots for copula fit based on this projection are adapted to deal with discrete variables. Covariates relating to within-match contributions such as numbers of passes and tackles are introduced to explain variability in shot outcomes. The results of this analysis would appear to support the notion that playing the 'beautiful game' is an effective strategy—more passes and crosses contribute to more effective play and more shots on the goal.     

A new bivariate distribution in natural exponential family

We propose a new bivariate distribution following a GLM form i.e., natural exponential family given the constantly correlated covariance matrix. The proposed distribution can represent an independent bivariate gamma distribution as a special case. In order to derive the distribution we utilize an integrating factor method to satisfy the integrability condition of the quasi-score function. The derived distribution becomes a mixture of discrete and absolute continuous distributions. The proposal of our new bivariate distribution will make it possible to develop some bivariate generalized linear models. Further the discrete correlated bivariate distribution will also arise from an independent bivariate Poisson mass function by compounding our proposed distribution (Iwasaki and Tsubaki, 2002).Received March 2003     

Food scandals,media attention and habit persistence among desensitised meat consumers

Consumer reactions to food scandals and their resulting economic implication are well documented. However, studies have typically neglected the roles that consumption habits and media usage behaviours may play in explaining household’s response to food safety incidences. In this study we develop a model of heterogeneous media usage intensity, information impacts and decay over time to estimate household’s behavioural responses to the 2011 German Dioxin scandal. We are specifically interested in determining the degree of heterogeneity in household’s short-term adjustments demand patterns versus persisting long-term consumption habits of meat products (chicken and pork) directly affected by the incident. The empirical analysis employs detailed household-level retail scanner and media usage data collected by the GfK Consumer Scan panel for a total of 16,023 households over a period of 104 calendar weeks. Results of dynamic correlated random effect Tobit models indicate an important role of unobserved heterogeneity in explaining household responses during the food scandal. We find strong empirical evidence supporting our hypothesis that short-term marginal adjustments in demand and propensity to buy affected products triggered by the negative impact of household media exposure were over-compensated by habit persistence. The question of how consumption patterns evolve over time in the presence of food scandals is expected to be of interest for both policy makers and the food industry. The potential biases in the projection of economic impacts resulting from simplifying assumptions of household’s response patterns to a proliferating numbers of food safety incidences has implications for risk management and public policy.     

混合型基金绩效评估及驱动因素分析——基于DEA-Tobit模型

将随机选出的混合型基金作为研究样本,采用DEA-Tobit二阶段模型进行研究.首先利用数据包络模型对样本进行绩效评估,在此基础上,利用Tobit模型研究相关指标对于基金绩效的影响机制.结果表明,基金本身所面临的风险收益因素以及基金经理团队稳定性对于基金绩效均具显著影响.最后结合实证结果就提升中国基金绩效提出相关的建议.     

我国三大经济区的区域化收益及其影响因素——基于城市经济效率的视角

经济区域化是目前我国城市经济发展的根本趋势,考察各类城市参与经济区域化的获益程度及其影响因素具有很强的现实意义。本文借鉴Byrnes和Storbeck(2000)的方法,基于城市经济效率的视角,考察了长三角、珠三角和京津冀地区的区域化收益水平,并基于Tobit模型对影响区域化收益的各种因素进行了计量分析。结果显示,我国三大经济区的区域化收益存在显著差异,京津冀地区的区域化收益最高,长三角地区次之,珠三角地区最低。进一步的计量分析发现,专业化分工和技术研发能力有利于区域化收益的实现,而外资的引进、政府的作用和城市空间影响则扩大了区域化收益,多样化分工和对外贸易对区域化收益的影响尚不明确,分地区的计量分析也基本上得出了同样的结论。加强区域内与区域间分工,是实现区域化收益和提升城市经济效率的根本途径。     

丝绸之路经济带物流业效率及其影响因素

物流业发展对丝绸之路经济带建设具有十分重要的意义,而效率是物流业发展的主要方面。近年来,我国物流业发展很快,但由于起步晚,与发达国家和地区相比仍然存在较大差距,尚未走出高成本、低效率的状态,提高物流业效率已经成为当务之急。以2004-2012年面板数据为基础,实证分析我国丝绸之路经济带物流业效率及其影响因素发现,丝绸之路经济带物流业全要素生产率受技术进步的影响呈波动趋势,年均下降2.1%,主要源于技术进步与规模效率的下降;各地区物流业全要素生产率均值为0.979,地区技术进步效率不高,其中宁夏物流业效率最高,云南物流业效率降幅最大;物流业专业化程度与物流业资源利用率对丝绸之路经济带物流业效率具有显著的正向影响;城镇化水平与开放程度有助于提升丝绸之路经济带物流业效率,但统计上并不显著。这在一定程度上表明,当前经济实力、产业结构、人力资本水平三个因素对提升丝绸之路经济带物流业效率尚不存在积极影响。为更好地提升丝绸之路经济带物流业效率,应进一步促进物流业技术进步及其推广应用;进一步加强一体化规划,提高物流业规模效率与资源利用率;进一步培育物流业集群与龙头企业,提高物流业专业化程度;进一步改善物流业发展的"软环境",提升物流业效率。     

丝绸之路河西走廊物流业效率演化分析

构建了分析河西走廊物流业效率演化的指标体系,基于2009-2017年河西走廊物流业统计数据,运用DEA-Malmquist指数法测算了各市物流业效率和技术进步率,采用Tobit回归模型分析了该地区物流业效率的影响因素,并从省内、省域间、各经济区域间三个方面进行了对比分析。实证分析表明,相较于全国各省市地区,河西走廊物流业整体水平较低,物流业效率和技术进步率的强正相关性普遍存在,其物流业发展对政策偏向具有较强的依赖性;同时,技术进步率低下和基础设施匮乏也是制约物流业发展的重要因素。基于此,从政策照顾与资金投入、科技进步与管理水平和市场运营与体系建设三个方面,为河西走廊物流业发展提出了建议与改善措施。