State–Space Forecasting of Schistosoma haematobium Time-Series in Niono, Mali

Adequate forecasting and early warning systems are based upon observations of human behavior, population, disease time-series, climate, environment, and/or a combination thereof, whichever option best compromises among realism, feasibility, robustness, and parsimony. Fully automatic and user-friendly state–space forecasting frameworks, incorporating myriad options (e.g., expert opinion, univariate, multivariate, and spatial-temporal), could considerably enhance disease control and hazard mitigation efforts in regions where vulnerability to neglected tropical diseases is pervasive and statistical expertise is scarce. The operational simplicity, generality, and flexibility of state–space frameworks, encapsulating multiple methods, could conveniently allow for 1) unsupervised model selection without disease-specific methodological tailoring, 2) on-line adaptation to disease time-series fluctuations, and 3) automatic switches between distinct forecasting methods as new time-series perturbations dictate. In this investigation, a univariate state–space framework with the aforementioned properties was successfully applied to the Schistosoma haematobium time-series for the district of Niono, Mali, to automatically generate contemporaneous on-line forecasts and hence, providing a basis for local re-organization and strengthening public health programs in this and potentially other Sahelian districts

IPO anomalies and innovation capital

Innovation capital are typically expensed and/or unrecognized as assets under current generally accepted accounting principles. This results in accounting-related information asymmetry. This paper examines the association of innovation capital (as measured here by the proxies of R&D expenditures and granted patents) and initial public offerings (IPO) anomalies. These anomalies include initial IPO underpricing, duration of honeymoon (a distinct feature of the Taiwanese IPO environment), and long-term performance. The theoretical model underlying this research is a signaling model. The results indicate that more innovative firms are more likely to be underpriced, and have longer honeymoon periods than less innovative firms. Further, the more innovative firms have positive and growing long-term market-adjusted returns. This stands in contrast to the declining long-term stock performance of initial public offering firms that is evidenced in the literature. We conclude that pre-IPO research and development expenditures disclosed in the IPO prospectus, official monthly reports of newly developed patents released to the public, and the frequency of patent citations significantly signal both underpricing and future market performance of IPO firms in Taiwan. Copyright Springer Science + Business Media, LLC 2006Anomalies, Initial public offerings, Innovation capital, Patent, R&D, Signaling,

Interrelationships among lean bundles and their effects on operational performance

The aim of our research is to disentangle the complex relations among lean bundles and operational performance. In particular, we focus on the direct and mediating effects on operational performance of three of the main lean manufacturing bundles, namely Just in Time (JIT), Total Quality Management (TQM) and Human Resource Management (HRM). We run statistical analysis on the High Performance Manufacturing round III database, a survey involving 266 plants in nine countries across three different industries. Our results show that JIT and TQM have a direct and positive effect on operational performance while HRM has a mediated effect on it. Theoretical and managerial implications of our findings are then drawn and discussed