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on Information and Communication Technologies |
| By: | Tai-Yoo Kim; Jihyoun Park; Eungdo Kim; Junseok Hwang (Technology Management, Economics, and Policy Program (TEMEP), Seoul National University) |
| Abstract: | The digital economy is one of the most important features of the knowledge-based society of the future. Based on information and communications technology (ICT), it grows faster than and eventually overtakes the traditional industrial economy. The fundamental driving forces of the digital economy’s faster economic growth are as follows. First, ICT converges with and improves the efficiency of traditional industries. Second, the production function of the ICT industry shows increasing returns to scale. Third, the development of ICT stimulates not only demand and supply but the entire expansive reproduction system, resulting in faster-accelerating economic growth. This paper investigates the essentials, causes, and patterns of the faster economic growth of the digital economy, and forecasts its future on the basis of real-life examples from the US, Finland, and Ireland. Furthermore, the core of the IT paradox is revisited, so that the potential of the digital economy can be reaffirmed. |
| Keywords: | Knowledge-based society, digital economy, new economy, economic growth, faster acceleration, technological change, IT paradox. |
| JEL: | L16 O11 O47 |
| Date: | 2011–04 |
| URL: | http://d.repec.org/n?u=RePEc:snv:dp2009:201173&r=ict |
| By: | Frisén, Marianne (Statistical Research Unit, Department of Economics, School of Business, Economics and Law, Göteborg University) |
| Abstract: | Multivariate surveillance is of interest in industrial production as it enables the monitoring of several components. Recently there has been an increased interest also in other areas such as detection of bioterrorism, spatial surveillance and transaction strategies in finance. Multivariate counterparts to the univariate Shewhart, EWMA and CUSUM methods have earlier been proposed. A review of general approaches to multivariate surveillance is given with respect to how suggested methods relate to general statistical inference principles. Multivariate on-line surveillance problems can be complex. The sufficiency principle can be of great use to find simplifications without loss of information. We will use this to clarify the structure of some problems. This will be of help to find relevant metrics for evaluations of multivariate surveillance and to find optimal methods. The sufficiency principle will be used to determine efficient methods to combine data from sources with different time lag. Surveillance of spatial data is one example. Illustrations will be given of surveillance of outbreaks of influenza. |
| Keywords: | Sequential; Surveillance; Multivariate; Sufficiency |
| JEL: | C44 |
| Date: | 2011–03–29 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:gunsru:2011_005&r=ict |