No abstract

At a time when the number of internationally mobile students worldwide has been growing steadily, the US share of this market has been declining. Since, as it is often claimed, international students are the best ambassadors for their host countries, an effective recruitment strategy is to enhance their learning experience, with the expectation that others will hear about it. In a post 9/11 environment, we focus on the importance of non-discriminatory environments to bring about successful learning outcomes, which we construe as academic performance and perceived quality of educational experience. We hypothesise that student engagement acts as a mediator in this relationship. We also investigate the moderating effect of perceived English proficiency in the relationship between discrimination and engagement. We find that (1) perceived discrimination has a strong, negative impact on educational experience, (2) the mediation primarily occurs through active and collaborative learning and (3) perceived English proficiency dampens the negative effects of discrimination on engagement. Our findings have important implications for university administrators involved in recruitment efforts.

Forecasting is a necessity almost in any operation. However, the tools of forecasting are still primitive in view of the great strides made by research and the increasing abundance of data made possible by automatic identification technologies, such as radio frequency identification (RFID). The relationship of various parameters that may change and impact decisions are so abundant that any credible attempt to drive meaningful associations are in demand to deliver the value from acquired data. This paper proposes some modifications to adapt an advanced forecasting technique (GARCH) with the aim to develop it as a decision support tool applicable to a wide variety of operations including supply chain management (SCM). We have made an attempt to coalesce a few different ideas toward a 'solutions' approach aimed to model volatility and in the process, perhaps, better manage risk. It is possible that industry, governments, corporations, businesses, security organizations, consulting firms and academics with deep knowledge in one or more fields, may spend the next few decades striving to synthesize one or more models of effective modus operandi to combine these ideas with other emerging concepts, tools, technologies and standards to collectively better understand, analyse and respond to uncertainty. However, the inclination to reject deep-rooted ideas based on inconclusive results from pilot projects is a detrimental trend and begs to ask the question whether one can aspire to build an elephant using mouse as a model.

This study evaluated the epidemiology of suspected cases of pandemic influenza A (H1N1) virus infection in [2009][2010] in Kurdistan province, a frontier province of the Islamic Republic of Iran. A questionnaire covering demographic characteristics, clinical presentation and outcome, and history of exposure and travel was completed by patients attending health centres and hospitals in the province. Nasal and throat swabs were analysed by RT-PCR. A total of 1059 suspected cases were assessed; H1N1 influenza A was confirmed in 157 (14.8%). The highest proportion of confirmed cases was 30.0%, among children aged < 1 year. In multivariate analysis, previous contact with symptomatic influenza patients (OR = 2.17) and hospitalization (OR = 3.88) were the only significant risk factors for confirmed H1N1 infection. Age, sex, residency, presenting symptoms and history of national or international travel were not significant. Influenza A (H1N1) virus has spread in Islamic Republic of Iran; probably transmitted by travellers to Kurdistan. RÉSUMÉ La présente étude visait à évaluer l'épidémiologie des cas suspectés d'infection par le virus de la grippe pandémique A(H1N1) en 2009-2010 dans la province du Kurdistan, une province frontalière de la République islamique d'Iran. Un questionnaire couvrant les caractéristiques démographiques, la présentation clinique et les résultats ainsi que les antécédents d'exposition et de déplacements a été rempli par les patients consultant dans les centres de santé et les hôpitaux de la province. Des prélèvements de nez et de gorge ont été analysés par PCR en temps réel. Au total, 1059 cas suspects ont été évalués ; le virus de la grippe A(H1N1) a été confirmé dans 157 cas (14,8 %). Le pourcentage le plus élevé de cas confirmés était 30 % chez les enfants de moins d'un an. À l' analyse multivariée, un contact antérieur avec des patients porteurs de symptômes de la grippe (OR = 2,17) et une hospitalisation (OR = 3,88) étaient les seuls facteurs de risque importants pour une infection à H1N1 confirmée. L'âge, le sexe, le lieu de résidence, les symptômes initiaux ainsi que des antécédents de voyages dans le pays ou à l'étranger n'étaient pas des facteurs significatifs. Le virus de la grippe A(H1N1) s'est propagé en République islamique d'Iran, probablement transmis par des voyageurs qui se rendaient au Kurdistan.

No abstract

iShares funds are products designed to mimic the movements of MSCI stock market indices. Being devoid of problems associated with trading restrictions, exchange-rate fluctuations and non-synchronous trading, iShares data are better suited for measuring, firstly, equity-market comovements and, secondly, diversification potential than national indices data; the latter data are used by most of the studies in the area. Applying recent time-varying methodology for the analysis of short-and long-term co-movements, a detailed analysis of the dynamics of the equity market linkages over the period 1996-2005 is provided. Evidence is found of increasing conditional correlations and significant time-varying long-run relationships between the US and the majority of other G7 markets since 2001, as measured by iShares. However, the extent of both short-term and long-term linkages between the G7 equity markets is lower for national indices data. Our findings suggest that (i) the results of earlier studies that are based on stock market indices should be interpreted with caution, since using these may overestimate the extent of available diversification benefits; and (ii) iShares funds do not represent perfect diversification products. These results appear to be robust to alternative model specifications, data frequency and conditioning bias.

The boom-bust cycle in U.S. house prices has been a fundamental determinant of the recent financial crisis leading up to the Great Recession. The risky financial innovations in the housing market prior to the recent crisis fueled the speculative housing boom. In this backdrop, the main objectives of this empirical study are to i) detect the possibility of multiple structural breaks in the US house price data during 1995-2010, exhibiting very sharp upturns and downturns; ii) endogenously determine the break points and iii) conduct house price forecasting exercises to see how models with structural breaks fare with competing time series modelslinear and nonlinear. Using a very general methodology (Bai-Perron, 1998, 2003, we found four break points in the trend in the S&P/Case-Shiller 10 city aggregate house-price index series. Next, we compared the forecasting performance of the model with structural breaks to four competing modelsnamely, Random Acceleration (RA), Autoregressive Moving Average (ARMA), Self-Exciting Threshold Autoregressive (SETAR), and Smooth Transition Autoregressive (STAR). Our findings suggest that house price series not only has undergone structural changes but also regime shifts. Hence, forecasting models that assume constant coefficients such as ARMA may not accurately capture house price dynamics.

This paper reexamines the role of the Federal Reserve in triggering the recent housing crisis. Specifically, we explore if the relationship between the federal funds rate and the housing variables underwent structural changes in the wake of the housing crisis. Using quarterly data spanning 1960–2017, we estimate a VAR model involving federal funds rate, real GDP growth and a housing variable (captured by house price inflation or residential investment share or housing starts) and conduct time series analysis for the pre- and post-crisis periods. While previous studies mostly set break-dates based on events known a priori to split the full sample to subsamples, we endogenously determine structural break points occurring at multiple unknown dates. Our Granger causality analysis indicates that the federal funds rate did not cause house price inflation, although it caused residential investment share and housing starts in the pre-crisis period. In the post-crisis period, the real GDP growth caused residential investment and housing starts while house price inflation had a momentum of its own. Our impulse response and forecast error variance decomposition analysis reinforce these results. Overall, our findings suggest that housing volume fluctuates more than house prices over the business cycle.