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.     

Forecasting Bordeaux wine prices using state-space methods

Generic Bordeaux red wine (basic claret) can be regarded as being similar to an agricultural commodity. Production volumes are substantial, they are traded at high frequency and the quality of the product is relatively homogeneous. Unlike other commodities and the top-end wines (which represent only 3% of the traded volume), there is no futures market for generic Bordeaux wine. Reliable forecasts of prices can to large extent replace this information deficiency and improve the functioning of the market. We use state-space methods with monthly data to obtain a univariate forecasting model for the average price. The estimates highlight the stochastic trend and the seasonality present in the evolution of the price over the period 1999 to 2016. The model predicts the path of wine prices out of sample reasonably well, suggesting that this approach is useful for making reasonably accurate forecasts of future price movements.     

Promoting cost efficiency and uniformity in parcel delivery centre locations and service areas: a GIS-based analysis

This paper provides a systematic framework to determine local delivery centre (LDC) locations and service areas to reduce delivery costs and balance the allocation of parcel delivery loads to promote sustainable LDC development. Based on a case study of a parcel distribution company in Korea, this study proposes two strategies for LDC service area delineation within each terminal service boundary that either minimises the total delivery time or balances the allocation of delivery loads. This research contributes to improving cost efficiency and balancing delivery load allocations in planning LDC locations and service areas that can potentially promote the long-term mutual sustainable development of parcel distribution companies and their local delivery counterparts.     

金融系统促进技术创新的作用机理与动态效应

技术创新是我们国家调整产业结构、转变增长方式的中心环节,从金融功能观的视角揭示金融系统促进技术创新的作用机理,并采用1985-2008年的时序数据,构建多项分布滞后模型和状态空间模型,就金融机构贷款对我国技术创新的影响进行实证研究,结果表明金融机构贷款对我国技术创新有着显著的促进作用。     

基于状态空间法的福建省各市环境承载力比较分析

文章首先建立完整的环境承载力评价指标体系，然后运用状态空间法对福建省各市的环境承载力状况进行了计算。通过分析计算结果，发现福建省各市压力类指标普遍过高，而承压类指标普遍不足，在未来的经济社会发展中将会逐渐遇到较大的环境制约，并提出了相应的发展建议。     

A STATE-SPACE PARTITIONING METHOD FOR PRICING HIGH-DIMENSIONAL AMERICAN-STYLE OPTIONS

The pricing of American-style options by simulation-based methods is an important but difficult task primarily due to the feature of early exercise, particularly for high-dimensional derivatives. In this paper, a bundling method based on quasi-Monte Carlo sequences is proposed to price high-dimensional American-style options. The proposed method substantially extends Tilley's bundling algorithm to higher-dimensional situations. By using low-discrepancy points, this approach partitions the state space and forms bundles. A dynamic programming algorithm is then applied to the bundles to estimate the continuation value of an American-style option. A convergence proof of the algorithm is provided. A variety of examples with up to 15 dimensions are investigated numerically and the algorithm is able to produce computationally efficient results with good accuracy.     

Descriptive statistics of large data sets by scatter plots, an exploratory approach

In the analysis of large tables of M variables on N observations one is interested in the relations between the variables and it is usual to inspect the M(M-1)/2 scatter plots of N points. Clearly, the scatter plot approach relies on visual inspection and is to be preferred in so far as applicable to detect simple relations, namely when M is small. Other approaches are needed for large values of M .
We consider that only the relatively few scatter plots that present a 'structure' are of interest for an exploratory analysis and, by 'structure', we mean a domain of specially high local density in the plot. Based on this concept, we propose a method constructed around two steps: the selection of the possibly interesting pairs of variables and the validation of the corresponding scatter plots. The selection of the pairs results from an algorithm based on a binary partitioning tree. The validation of the corresponding scatter plots enables the production of only those where a structure is found the recognition of a structure is derived from a statistic based on the length of the Minimum Spanning Tree constructed on the N points of the candidate scatter plot.
For illustration, we report on an industrial application where the method is routinely applied for exploratory purposes.     

一种新的机器人自组网群组运动控制模型

针对现有机器人自组网运动控制模型无法真实反映机器人运动规律的问题,提出一种新的群组运动控制模型(group movement control model,GMCM)。该模型基于机器人通用运动学模型,采用群组控制算法,对传统群组随机运动模型——参考点群组运动模型(reference point groupmobility,RPGM)进行修改,可描述机器人编队解散和集合的运动状态,满足群组分割与合并的需求。进一步实现GMCM模型,并基于GMCM,RPGM,DRGM模型对机器人自组网路由协议进行模拟仿真。结果表明,基于GMCM模型仿真时协议性能有所降低,较为真实地模拟了机器人编队运动,对机器人自组网的研究具有一定的参考价值。     

Low Mortgage Rates and Securitization: A Distinct Perspective on the US Housing Boom*

In this paper, we analyse the impacts of low interest rates and lax underwriting standards on the US housing boom around the beginning of the new millennium. We suggest a time-varying mean of the log price-to-rent ratio (PtR) to capture the persistent changes in housing prices. We show that the increasing latent trend in the PtR was significantly affected by the increased securitization of residential mortgage loans and decreasing interest rates, with the former effect being about three times larger than the latter. In the absence of securitization, negative interest rates would have been needed to reproduce an equally large housing boom since 2003.     

Integrated Berth Allocation and Quay Crane Assignment Problem: Set partitioning models and computational results

Most of the operational problems in container terminals are strongly interconnected. In this paper, we study the integrated Berth Allocation and Quay Crane Assignment Problem in seaport container terminals. We will extend the current state-of-the-art by proposing novel set partitioning models. To improve the performance of the set partitioning formulations, a number of variable reduction techniques are proposed. Furthermore, we analyze the effects of different discretization schemes and the impact of using a time-variant/invariant quay crane allocation policy. Computational experiments show that the proposed models significantly improve the benchmark solutions of the current state-of-art optimal approaches.