A MONETARY BUSINESS CYCLE MODEL FOR INDIA

A New Keynesian monetary business cycle model is constructed to study why monetary transmission in India is weak. Our models feature banking and financial sector frictions as well as an informal sector. The predominant channel of monetary transmission is a credit channel. Our main finding is that base money shocks have a larger and more persistent effect on output than an interest rate shock, as in the data. The presence of an informal sector hinders monetary transmission. Contrary to the consensus view, financial repression in the form of a statutory liquidity ratio and administered interest rates, does not weaken monetary transmission. (JEL E31, E32, E44, E52, E63)     

A Decision Support System for Production/ Distribution Planning in Continuous Manufacturing

This paper presents a decision support system (DSS) for managing production/distribution planning in a continuous manufacturing environment. The vendor has multiple plants and distribution centers (DCs). The trading partners have widely varying independent demand patterns. The DSS is designed for use in a multiproduct environment with overlapping raw materials and processing requirements. The production and distribution lead times at plants may span multiple planning periods. The impact of any manual override of a suggested solution can also be evaluated. The DSS is based on a linear programming model with a rolling horizon and was originally designed for a large process industry. Results of a pilot implementation using actual data are also presented, which show potential for significant savings for the company.     

Identity tensions of women with two leadership positions in India

Identity narratives enhance the understanding of women’s leadership, but there exist very few in-depth analyses of negative identity tensions that influence agency. In this study, we examine the negative identity tensions of well-to-do women from small towns in India, who hold two leadership positions: organizational and social movement. We borrow from the discourse on well-to-do women’s participation in social movements in India to draw on the notions of identity, perceptions of feminism, and patriarchal challenge. Our data are derived from 49 in-depth interviews with women leaders. Findings from qualitative analysis and creation of a composite narrative show that negative identity tensions arising from two leadership positions are gendered in nature. Furthermore, agency is (i) contingent on one’s reflection on challenges, (ii) rooted in an underlying principle, and (iii) practiced through the mechanisms of “managing femininity,” a concept that is widely discussed in the Western paradigms of postfeminism and neoliberalism. This paper contributes to the feminist dialog on the global South in the context of gender, class, and geographical location intersection by revealing certain non-Western ways of managing femininity. However, in the process, the hegemony of Indian men remains intact.     

Order-free co-regionalized areal data models with application to multiple-disease mapping

With the ready availability of spatial databases and geographical information system software, statisticians are increasingly encountering multivariate modelling settings featuring associations of more than one type: spatial associations between data locations and associations between the variables within the locations. Although flexible modelling of multivariate point-referenced data has recently been addressed by using a linear model of co-regionalization, existing methods for multivariate areal data typically suffer from unnecessary restrictions on the covariance structure or undesirable dependence on the conditioning order of the variables. We propose a class of Bayesian hierarchical models for multivariate areal data that avoids these restrictions, permitting flexible and order-free modelling of correlations both between variables and across areal units. Our framework encompasses a rich class of multivariate conditionally autoregressive models that are computationally feasible via modern Markov chain Monte Carlo methods. We illustrate the strengths of our approach over existing models by using simulation studies and also offer a real data application involving annual lung, larynx and oesophageal cancer death-rates in Minnesota counties between 1990 and 2000.     

Flexible Cure Rate Modeling Under Latent Activation Schemes

With rapid improvements in medical treatment and health care, many datasets dealing with time to relapse or death now reveal a substantial portion of patients who are cured (i.e., who never experience the event). Extended survival models called cure rate models account for the probability of a subject being cured and can be broadly classified into the classical mixture models of Berkson and Gage (BG type) or the stochastic tumor models pioneered by Yakovlev and extended to a hierarchical framework by Chen, Ibrahim, and Sinha (YCIS type). Recent developments in Bayesian hierarchical cure models have evoked significant interest regarding relationships and preferences between these two classes of models. Our present work proposes a unifying class of cure rate models that facilitates flexible hierarchical model-building while including both existing cure model classes as special cases. This unifying class enables robust modeling by accounting for uncertainty in underlying mechanisms leading to cure. Issues such as regressing on the cure fraction and propriety of the associated posterior distributions under different modeling assumptions are also discussed. Finally, we offer a simulation study and also illustrate with two datasets (on melanoma and breast cancer) that reveal our framework's ability to distinguish among underlying mechanisms that lead to relapse and cure.     

Methods of constructing group divisible designs with k = 3 and 4

The paper provides methods of construction of group divisible designs with block sizes three and four through balanced incomplete block and partially balanced incomplete block designs of block sizes 3 and 4. Furthermore, four non-isomorphic solutions are given.     

Inference on finite population categorical response: nonparametric regression-based predictive approach

Suppose that a finite population consists of N distinct units. Associated with the ith unit is a polychotomous response vector, d _i , and a vector of auxiliary variable x _i . The values x _i ’s are known for the entire population but d _i ’s are known only for the units selected in the sample. The problem is to estimate the finite population proportion vector P. One of the fundamental questions in finite population sampling is how to make use of the complete auxiliary information effectively at the estimation stage. In this article a predictive estimator is proposed which incorporates the auxiliary information at the estimation stage by invoking a superpopulation model. However, the use of such estimators is often criticized since the working superpopulation model may not be correct. To protect the predictive estimator from the possible model failure, a nonparametric regression model is considered in the superpopulation. The asymptotic properties of the proposed estimator are derived and also a bootstrap-based hybrid re-sampling method for estimating the variance of the proposed estimator is developed. Results of a simulation study are reported on the performances of the predictive estimator and its re-sampling-based variance estimator from the model-based viewpoint. Finally, a data survey related to the opinions of 686 individuals on the cause of addiction is used for an empirical study to investigate the performance of the nonparametric predictive estimator from the design-based viewpoint.     

Self-reported Use of Emotional Display Rules in the Netherlands and Iran: Evidence for Sociocultural Influence

Sociocultural differences in children's use and understanding of emotional display rules have been under-researched. In the present study, 56 Dutch and 56 Iranian children aged 10–11 years took part in a structured interview about their experiences of using emotional display rules. In comparison with the Dutch children, the Iranian sample was more likely to report having actually used emotional display rules themselves, more likely to identify family audiences for display rules, and less likely to identify peer audiences. In addition, they were more likely than the Dutch children to identify both prosocial and self-protective motives for concealing emotion from family audiences, and less likely to identify self-protective motives for concealing emotion from peers. Results are interpreted in the light of socialization processes involved in the development of emotion regulation.