The multiple filter test for change point detection in time series

A framework for the detection of change points in the expectation in sequences of random variables is presented. Specifically, we investigate time series with general distributional assumptions that may show an unknown number of change points in the expectation occurring on multiple time scales and that may also contain change points in other parameters. To that end we propose a multiple filter test (MFT) that tests the null hypothesis of constant expectation and, in case of rejection of the null hypothesis, an algorithm that estimates the change points.The MFT has three important benefits. First, it allows for general distributional assumptions in the underlying model, assuming piecewise sequences of i.i.d. random variables, where also relaxations with regard to identical distribution or independence are possible. Second, it uses a MOSUM type statistic and an asymptotic setting in which the MOSUM process converges weakly to a functional of a Brownian motion which is then used to simulate the rejection threshold of the statistical test. This approach enables a simultaneous application of multiple MOSUM processes which improves the detection of change points that occur on different time scales. Third, we also show that the method is practically robust against changes in other distributional parameters such as the variance or higher order moments which might occur with or even without a change in expectation. A function implementing the described test and change point estimation is available in the R package MFT.     

Hybrid estimators for small diffusion processes based on reduced data

We deal with the Bayes type estimators and the maximum likelihood type estimators of both drift and volatility parameters for small diffusion processes defined by stochastic differential equations with small perturbations from high frequency data. From the viewpoint of numerical analysis, initial Bayes type estimators for both drift and volatility parameters based on reduced data are required, and adaptive maximum likelihood type estimators with the initial Bayes type estimators, which are called hybrid estimators, are proposed. The asymptotic properties of the initial Bayes type estimators based on reduced data are derived and it is shown that the hybrid estimators have asymptotic normality and convergence of moments. Furthermore, a concrete example and simulation results are given.     

Extreme value statistics for censored data with heavy tails under competing risks

This paper addresses the problem of estimating, from randomly censored data subject to competing risks, the extreme value index of the (sub)-distribution function associated to one particular cause, in a heavy-tail framework. Asymptotic normality of the proposed estimator is established. This estimator has the form of an Aalen-Johansen integral and is the first estimator proposed in this context. Estimation of extreme quantiles of the cumulative incidence function is then addressed as a consequence. A small simulation study exhibits the performances for finite samples.     

Market-based capabilities and financial performance of firms: insights into marketing’s contribution to firm value

While there is recognition that market-based capabilities contribute to a firm’s financial performance, the exposition is largely conceptual (Srivastava et al. Journal of Marketing 62:2–18, 1998; Journal of Marketing 63:168–179, 1999). Using a resource based view of the firm, the present study proposes that (1) market-based assets and capabilities of a firm impacts (2) performance in three market-facing business processes (new product development, supply-chain and customer management), which in turn, influence (3) the firm’s financial performance. It develops related hypotheses and tests the framework empirically. The study also examines for the first time the interrelationship among the three business processes and their impact on the market value of firms. Further, the study examines the moderating influence of two organizational variables—size and age of the firm. Overall, the major contribution of the study is that it offers a process linkage between capabilities, process performance and financial performance. The results of this research will provide strategic insights to managers on optimal customer management, product development and supply chain strategies.

Evaluation of subsidiary marketing performance: combining process and outcome performance metrics

Issues in evaluating marketing performance and devising appropriate metrics for measurement have taken center stage in marketing thought and practice in recent years. We propose an empirical model that enables a multinational enterprise (MNE) to assess the marketing performance of its subsidiaries, taking into explicit consideration the fact that tactical actions by subsidiaries contribute to the creation of assets that can be harnessed for marketing outcomes. Thus, our model captures the asset creation abilities of marketing expenditures and also takes in to account the environmental differences of the context in which each MNE subsidiary operates. We evaluate comparative, overall, and process-level (creation of market assets and market yield) marketing performance in the context of multi-country operations. This simultaneous examination of marketing process and marketing outcome performance enables a global corporation to gain strategic, operational, and diagnostic insights into the performance of its subsidiaries. Our approach is empirically illustrated with an evaluation of the marketing performance of subsidiaries of a large global corporation.     

When do the stock market returns to new product preannouncements predict product performance? Empirical evidence from the U.S. automotive industry

Substantial research has examined how stock market reactions to marketing actions affect subsequent marketing decisions. However, prior research provides limited insights into whether abnormal stock returns to a marketing action actually predict the future performance resulting from that action. This study focuses on new product preannouncements (NPPAs) and investigates the relationship between short-term stock market returns to an NPPA and the post-launch new product performance under various industry and firm conditions. Findings based on a dynamic panel data analysis of 208 NPPAs in the U.S. automotive industry between 2001 and 2014 reveal that stock returns associated with an NPPA are not an appropriate forward-looking measure of future product performance. However, under specific conditions (i.e., when the preannouncement is specific, the preannounced new product has low innovativeness, the preannouncing firm has a high reputation and invests heavily in advertising, and the preannouncement environment is less competitive), abnormal stock returns to NPPAs actually predict the future performance of new products. Thus, this study extends the marketing–finance and innovation literature with its focus on the conditions that affect the predictive power of immediate stock returns for the future performance of new products.     

Augmenting the eye of the beholder: exploring the strategic potential of augmented reality to enhance online service experiences

Driven by the proliferation of augmented reality (AR) technologies, many firms are pursuing a strategy of service augmentation to enhance customers’ online service experiences. Drawing on situated cognition theory, the authors show that AR-based service augmentation enhances customer value perceptions by simultaneously providing simulated physical control and environmental embedding. The resulting authentic situated experience, manifested in a feeling of spatial presence, functions as a mediator and also predicts customer decision comfort. Furthermore, the effect of spatial presence on utilitarian value perceptions is greater for customers who are disposed toward verbal rather than visual information processing, and the positive effect on decision comfort is attenuated by customers’ privacy concerns.     

Managed care and the near-elderly: effects of plan enrollment on functionality

This paper examines the effects of enrollment in a health maintenance organization (HMO) or a preferred provider organization (PPO) on the functional status of near-elderly adults (aged 55–64), compared to traditional fee-for-service (FFS) plans. A sample of 1306 near-elderly adults with employer-sponsored health insurance are drawn from the 2000–2002 waves of the Health and Retirement Study, a nationally representative panel survey of community-dwelling adults. Regression models are estimated to assess the effects of different types of insurance plans on functionality, as measured by whether or not the individual has any functional limitations. The potential influence of selection bias into alternative types of plans is addressed by limiting the sample to near-elders without a choice of health plans. The effects of HMOs on functionality are shown to be comparable to those of FFS plans among the general near-elderly population. However, significant adverse effects of HMO enrollment on functional status are observed among near-elders with chronic conditions. PPO enrollees have similar functional outcome to FFS enrollees, even among those with chronic conditions. The observed differences in functional outcome across plans have important implications for the practicality of managed care plans serving older adults.     

Will the skill-premium in the Netherlands rise in the next decades?

While the skill-premium has been rising sharply in the US and the UK for 20 years, the Dutch skill-premium decreased for much of that period and only started to rise in the early 90s. In this article, we investigate whether the Dutch skill-premium will rise in the next decades. To answer this question, we forecast the skill-premium using the Katz and Murphy (1992 Katz, L and Murphy, K. 1992. Changes in relative wages, 1963–1987: supply and demand factors. Quarterly Journal of Economics, 107: 35–78. [Crossref], [Web of Science ®] , [Google Scholar]) and the Krusell et al. (2000 Krusell, P, Ohanian, L, Ríos-Rull, J-V and Violante, G. 2000. Capital-skill complementarity and inequality: a macroeconomic analysis. Econometrica, 68: 1029–53. [Crossref], [Web of Science ®] , [Google Scholar]) models. The Katz and Murphy model (KM) explains demand shifts by skill-biased technological change in unobservable variables captured by a time trend. In contrast, the Krusell et al. model (KORV) explains demand shifts by (observable) changes in the capital stock under a capital-skill complementarity technology. The results show that while the KM model predicts that the skill-premium will have increased by 30% in 2020, based on realistic predictions of the stock of capital, the KORV model predicts that the skill-premium will remain between ?5 and +5% of its 1996 level.