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on Information and Communication Technologies |
| By: | Fok, D. |
| Abstract: | The widespread use of the Internet and computer systems has led to a situation where data are available on almost everything. The volume and the level of detail of these data is something we considered to be impossible until a few years ago. Researchers in economics and business now have access to a new variety of data. Such data are often not directly comparable to the data that have historically been considered in econometrics. This brings opportunities and challenges for academic researchers, companies, and even econometrics as a field and as an educational program. All parties involved can, or even have to use these data to stay ahead of the competition. |
| Keywords: | Bayesian inference, Big data, Business data, Data mining, Econometrics, Forecasting, Quantitative marketing |
| JEL: | C44 M31 C80 |
| Date: | 2013–10–04 |
| URL: | http://d.repec.org/n?u=RePEc:ems:euriar:41515&r=ict |
| By: | Marcelo Bianconi (Department of Economics, Tufts University, USA); Xiaxin Hua (Department of Economics, Clark University, USA); Chih Ming Tan (Department of Economics, University of North Dakota, USA) |
| Abstract: | We study the effects of two measures of information dissemination on the determination of systemic risk. One measure is print-media consumer sentiment based while the other is volatility based. We find evidence that while the volatility measure (VIX) of future expectations has a more significant direct impact upon systemic risk of financial firms under distress, a consumer sentiment measure based on print-media news does impact upon firm’s financial stress via the externality of other firm’s financial stress. This latter effect is robust even though the VIX and the consumer sentiment have dynamic feedback in the short one and two-day horizon in levels, and contemporaneously in volatility. In reference to the internet bubble of the 1990s, the consumer sentiment measure predicts larger systemic risk in the whole period of exuberance while the VIX predicts a sharp larger systemic risk in the height of the bubble. Our evidence suggests that print-media consumer sentiment might be dominated by the VIX when predicting systemic risk. |
| Keywords: | conditional value-at-risk, VIX, externality, consumer sentiment |
| JEL: | G00 G14 |
| Date: | 2013–12 |
| URL: | http://d.repec.org/n?u=RePEc:rim:rimwps:67_13&r=ict |
| By: | McGregor, Elaine (UNU-MERIT / MGSoG); Siegel, Melissa (UNU-MERIT / MGSoG) |
| Abstract: | The use of internet technologies in daily life has risen dramatically in recent years, increasing researchers' interest in how social media such as Facebook, Twitter and LinkedIn are changing social realities and potentially facilitating innovative research methodologies. As technology and migration are considered prominent drivers of the globalization processes, the increasing interest of migration researchers is unsurprising. Nevertheless, given the relative youth of research in this field, approaches to the topic differ. By taking a step back and viewing the literature from a wide range of disciplines, this paper provides a broad overview of the current state of research on migration and social media in four key areas: 1) the use of social media to trigger and facilitate migration in both positive (networks) and negative ways (human trafficking); 2) the role of social media and migrant integration; 3) the use of social media in diaspora engagement; and 4) the use of social media in conducting migration research. This paper adds to the literature by being the first systematic review of the topic. |
| Keywords: | Social Media, Migration Research, Social Networking Sites, Diaspora Engagement, Integration, Facilitation of Migration |
| JEL: | F22 O15 O33 L86 L82 Q55 |
| Date: | 2013–12–31 |
| URL: | http://d.repec.org/n?u=RePEc:unm:unumer:2013068&r=ict |
| By: | Eric Lammers |
| Keywords: | hospitals; physicians , vertical integration, health information technology, transaction cost economics |
| JEL: | I |
| Date: | 2013–10–31 |
| URL: | http://d.repec.org/n?u=RePEc:mpr:mprres:7993&r=ict |
| By: | Daniele Bregantini; Jacco J.J. Thijssen |
| Abstract: | In this paper we propose a solution to the Bayesian problem of a decision maker who chooses, while observing trial evidence, an optimal stopping time at which either to invest in a newly developed health care technology or abandon research. We show how optimal stopping boundaries can be computed as a function of the observed cumulative net benefit derived from the new health care technology. At the optimal stopping time, the decision taken is optimal and the decision maker either invest or abandon the technology with consequent health benefits to patients. The model takes into account the cost of decision errors and explicitly models these in the payoff to the heath care system. The implications in terms of opportunity costs of decisions taken at sub-optimal time is discussed and put in the value of information framework. In a case study it is shown that the proposed method, when compared with traditional ones, gives substantial economic gains both in terms of QALYs and reduced trial costs. |
| Keywords: | Optimal stopping, HTA, Bayes, Value of Information |
| JEL: | C11 C12 C44 |
| Date: | 2014–01 |
| URL: | http://d.repec.org/n?u=RePEc:yor:yorken:14/01&r=ict |