A new financial stress index model based on support vector regression and control chart

Financial stress index (FSI) is considered to be an important risk management tool to quantify financial vulnerabilities. This paper proposes a new framework based on a hybrid classifier model that integrates rough set theory (RST), FSI, support vector regression (SVR) and a control chart to identify stressed periods. First, the RST method is applied to select variables. The outputs are used as input data for FSI–SVR computation. Empirical analysis is conducted based on monthly FSI of the Federal Reserve Bank of Saint Louis from January 1992 to June 2011. A comparison study is performed between FSI based on the principal component analysis and FSI–SVR. A control chart based on FSI–SVR and extreme value theory is proposed to identify the extremely stressed periods. Our approach identified different stressed periods including internet bubble, subprime crisis and actual financial stress episodes, along with the calmest periods, agreeing with those given by Federal Reserve System reports.     

Family ties and economic stability concerns of migrant labour families in Jordan

74 labor migrant families from various socioeconomic classes in Amman, Jordan were interviewed to examine changes in relationships among family members, extended family, and neighbors and their concerns about economic stability in the host country, Jordan, and the world market. Another purpose was to determine how current migration policies of the Arab oil-producing countries which prohibit labor migrants from bringing their families to the host country affect labor migration among families. The families consisted of either those who did or did not accompany the labor migrant. Overall labor migration affected unaccompanied families more than accompanied families, e.g., only 19% of the unaccompanied families reported increased family unity compared with 56% of accompanied families. Problems within unaccompanied families increased in 43% of the cases but in only 6% of the accompanied families. Many of these problems resulted in children dropping out of school which reflected the control fathers had within the family, separation, or divorce. Yet labor migration reduced family ties with extended family members and neighbors almost equally for both groups. Accompanied families were not as concerned about economic stability in Jordan as unaccompanied families (38% vs. 50%). Perhaps these families tended not to invest remittances received from the labor migrants working in Arab oil-producing countries in Jordan. Both groups were quite concerned about the economic stability in the host countries (66% and 72%, respectively) and the world market (59% and 62%, respectively), however. Since family unity suffers when families do not accompany labor migrants, it is suggested that oil-producing nations that depend on foreign labor should guarantee family unity as a human right.     

Transductive versions of the LASSO and the Dantzig Selector

Transductive methods are useful in prediction problems when the training dataset is composed of a large number of unlabeled observations and a smaller number of labeled observations. In this paper, we propose an approach for developing transductive prediction procedures that are able to take advantage of the sparsity in the high dimensional linear regression. More precisely, we define transductive versions of the LASSO (Tibshirani, 1996) and the Dantzig Selector (Candès and Tao, 2007). These procedures combine labeled and unlabeled observations of the training dataset to produce a prediction for the unlabeled observations. We propose an experimental study of the transductive estimators that shows that they improve the LASSO and Dantzig Selector in many situations, and particularly in high dimensional problems when the predictors are correlated. We then provide non-asymptotic theoretical guarantees for these estimation methods. Interestingly, our theoretical results show that the Transductive LASSO and Dantzig Selector satisfy sparsity inequalities under weaker assumptions than those required for the “original” LASSO.     

Spatio-temporal patterns of pre-eclampsia and eclampsia in relation to drinking water salinity at the district level in Bangladesh from 2016 to 2018

Population and Environment - This analysis examines whether salinity in drinking water is associated with pre-eclampsia and eclampsia (PE/E), a leading cause of maternal morbidity and mortality....     

Population aging and long-term care policies in the Gulf region: a case study of Oman

Population aging is a phenomenon occurring across the globe including in countries traditionally exhibiting population dividends and “youth bulges.” The Gulf Corporation Council countries are no exception as they currently experience a process of population aging, albeit at a different stage from many developed countries. However, due to historically high fertility rates and fast-paced epidemiological transition, some of these countries will experience population aging at a higher pace than what has been observed in Europe and the United States. This article reviews recent developments in long-term care policies in the Gulf region with a focus on Oman as an example of a high-income Arab country that is experiencing population aging while still being governed by traditional family aged-care norms. Utilizing existing data and published research complemented by policy analysis and field visits, we analyze the process of population aging in Oman and neighboring countries and its policy implications.     

Asymptotic expansions for sequential confidence levels in inverse linear regression

Non-linear renewal theory is used to derive second order asymptotic expansions for the coverage probability of a fixed-width sequential confidence interval for an unknown parameter xin the inverse linear regression model. These expansions are obtained for a two-stage sequential procedure, proposed by Perng and Tong (1974) for the construction of a confidence interval for x.