On conditional variance estimation in nonparametric regression

In this paper we consider a nonparametric regression model in which the conditional variance function is assumed to vary smoothly with the predictor. We offer an easily implemented and fully Bayesian approach that involves the Markov chain Monte Carlo sampling of standard distributions. This method is based on a technique utilized by Kim, Shephard, and Chib (in Rev. Econ. Stud. 65:361–393, 1998) for the stochastic volatility model. Although the (parametric or nonparametric) heteroscedastic regression and stochastic volatility models are quite different, they share the same structure as far as the estimation of the conditional variance function is concerned, a point that has been previously overlooked. Our method can be employed in the frequentist context and in Bayesian models more general than those considered in this paper. Illustrations of the method are provided.     

Non-profit organisations in India: historical development and common patterns

The article documents the history of the Indian voluntary or non-profit sector involved in socio-economic development of the country. Specifically, three questions are addressed. What type of voluntary organisations existed at what periods of history, and what were their primary activities? Who were the founders, and what were their motives? Can we detect common themes or underlying patterns in the way in which the Indian voluntary sector has developed? Or in other words: what is the institutional genesis of the non-profit or voluntary sector in India? The findings are based on multiple sources — literature review, interviews and observation, and information requested through the mail. The article differs from most historical studies on Indian non-profit organisations because it takes an analytical approach by drawing from contemporary literature on such organisations.     

DSGE Models with Student-t Errors

This paper deals with Dynamic Stochastic General Equilibrium (DSGE) models under a multivariate student-t distribution for the structural shocks. Based on the solution algorithm of Klein (2000) and the gamma-normal representation of the t-distribution, the TaRB-MH algorithm of Chib and Ramamurthy (2010 Chib , S. , Ramamurthy , S. ( 2010 ). Tailored randomized block MCMC methods with application to DSGE models . Journal of Econometrics 108 : 19 – 38 .[Crossref], [Web of Science ®] , [Google Scholar]) is used to estimate the model. A technique for estimating the marginal likelihood of the DSGE student-t model is also provided. The methodologies are illustrated first with simulated data and then with the DSGE model of Ireland (2004 Ireland , P. N. ( 2004 ). Technology shocks in the new keynesian model . Review of Economics and Statistics 86 ( 4 ): 923 – 936 .[Crossref], [Web of Science ®] , [Google Scholar]) where the results support the t-error model in relation to the Gaussian model.     

A location–allocation model for service providers with application to not-for-profit health care organizations

In this paper, we develop and solve a model for the location and allocation of specialized health care services such as traumatic brain injury (TBI) treatment. The model is based on and applied to one of the Department of Veterans Affairs’ integrated service networks. A cost minimization model with service proportion requirements is solved using simulated annealing. Large instances of the model with 100 candidate medical center locations and 15 open treatment units are solved in about 1000 s. In order to test the real-world applicability of our model, an extensive managerial experiment is conducted using data derived from our health care setting. In this experiment, the effects of three critical factors: (1) degree of centralization of services, (2) the role of patient retention as a function of distance to a treatment unit, and (3) the geographic density of the patient population are investigated with respect to the important trade-off between the cost of providing service and the need to provide such service. Our analysis shows that all three factors of the experiment are both relevant and useful to decision-makers when selecting locations for their services.     

Does Hospitalization Make Elderly Households Poor? An Examination of the Case of Kerala,India

Physical and health risks are very high among aged persons. The precise implications of population ageing for future levels of health and healthcare utilization depend on whether the increases in life expectancy experienced in general are accompanied by an increase or a decrease in health problems in later life. The health risks of the presence of an aged person in a household can result in a catastrophic shock for the family and render such households more exposed to poverty. The increased cost of medical bills means that large numbers of the elderly in the developing world are deprived of access to health and to better health treatment. This article examines this question empirically, using the largest national survey in the Indian state of Kerala, comparing elderly with non‐elderly households. We also attempt to find the impact of unhealthy lifestyles on the financial status of these households, due to hospitalization as a result of an aged person's illness.     

A Model for Resource Constrained Production and Inventory Management

We present a general model for multi-item production and inventory management problems that include a resource restriction. The decision variables in the model can take on a variety of interpretations, but will typically represent cycle times, production batch sizes, number of production runs, or order quantities for each item. We consider environments where item demand rates are approximately constant and performing an activity such as producing a batch of a product or placing an order results in the consumption of a scarceresource that is shared among the items. Some examples of shared resources include limited machine capacity, a restriction on the amount of money that can be tied up in stock, orlimited storage capacity. We focus on the case where the decision variables must be integer valued or selected from a discrete set of choices, such as when an integer number of production runs is desired for each item, or in order quantity problems where the items come in pack sizes containing more than one unit and, therefore, the order quantities must be an integer multiple of the pack sizes. We develop a heuristic and a branch and bound algorithm for solving the problem. The branch and bound algorithm includes reoptimization procedures and the heuristic to improve its performance. Computational testing indicates that the algorithms are effective for solving the general model.     

Globalization and the Status of Current Research on the Indian Nonprofit Sector

An overview of recent trends in research on the Indian nonprofit sector is presented. The material is not exhaustive of all research that has been conducted, but instead discusses effects of globalization on the literature. As used here, globalization implies the worldwide rise of economic liberalism, universal trust in political democracy, the advent of cultural universalism, relative erosion of the power of nation-states, and global embracing of capitalism and commodity culture. The following distinct effects of globalization are discussed: diverse policy debates on nongovernmental organization (NGO) roles in development, challenges to the credibility of India's most popular and debated theory of nonprofits, the emergence of a large volume of literature on environmental and women's movements and organizations, and the shifting of attention to the study of NGOs. A deliberate effort is made to identify the backgrounds of some of the authors discussed in the article to direct attention to differences in content of the writings of NGO officials, activists, scholars, policy analysts, development consultants, Westerners, and Indians.     

Likelihood Inference for Discretely Observed Nonlinear Diffusions

This paper is concerned with the Bayesian estimation of nonlinear stochastic differential equations when observations are discretely sampled. The estimation framework relies on the introduction of latent auxiliary data to complete the missing diffusion between each pair of measurements. Tuned Markov chain Monte Carlo (MCMC) methods based on the Metropolis‐Hastings algorithm, in conjunction with the Euler‐Maruyama discretization scheme, are used to sample the posterior distribution of the latent data and the model parameters. Techniques for computing the likelihood function, the marginal likelihood, and diagnostic measures (all based on the MCMC output) are developed. Examples using simulated and real data are presented and discussed in detail.     

Multiperiod Capacity Expansion in Globally Dispersed Regions

Multiperiod capacitated location (MCL) models specify where and when capacity expansions should be made, and how large they should be. The MCL model developed in this paper incorporates a shift from manufacturing for overseas markets to manufacturing in overseas markets. While MCL models generally have not provided lower bounds on the optimal solution, the methodology of this paper provides both upper and lower bounds. Computational results are given for problems involving up to 200 locations/destinations and 10‐year planning horizons. Near‐optimal solutions are provided in reasonable computing times with average convergence less than 2%. Representative variations in cost between regions are simulated in the test problems, and the managerial implications of alternative diversification strategies are also assessed.     

A Heuristic Algorithm for the Capacitated Multiple Supplier Inventory Grouping Problem

This paper presents and solves a model for the multiple supplier inventory grouping problem, which involves the minimization of logistics costs for a firm that has multiple suppliers with capacity limitations. The costs included in the model are purchasing, transportation, ordering, and inventory holding, while the firm's objective is to determine the optimal flows and groups of commodities from each supplier. We present an algorithm, which combines subgradient optimization and a primal heuristic, to quickly solve the multiple supplier inventory grouping problem. Our algorithm is tested extensively on problems of various sizes and structures, and its performance is compared to that of OSL, a state-of-the-art integer programming code. The computational results indicate that our approach is extremely efficient for solving the multiple supplier inventory grouping problem.