India's demand for international reserve and monetary disequilibrium: Reserve adequacy under floating regime

The main aim of this study is to investigate India's demand for international reserve by focusing on the role of national monetary disequilibrium and to present new benchmarks for assessing the adequacy of international reserves. We assessed India's position in terms of reserve adequacy and found that India is well placed and has sufficient stock of international reserves to meet the minimum adequacy requirements. Also, the results reveal that the central bank is holding substantial excess reserves and the related opportunity cost (1.5% of GDP) appears to be quite considerable. Further, the estimates of reserve demand function suggest that scale of foreign trade, uncertainty and profitability considerations play significant role in determining India's long-term reserve demand policies. More importantly, validating the monetary approach to balance of payment, our results show that national monetary disequilibrium does play a crucial role in short-run reserve movements. An excess of money demand (supply) induces an inflow (outflow) of international reserves with an elasticity of 0.56 which also implies that Reserve Bank of India responds to correct the domestic money market disequilibrium; and did not just leave it completely on the mercy of reserve inflows.     

Supply Chain Consequences of Subsidies for Corporate Social Responsibility

The use of government incentives tied to market prices as means of boosting corporate social responsibility (CSR) has expanded notably in recent decades. Enhanced business tax deductions for charitable donations and credits for conservation easements are notable cases. While providing incentives for socially desirable behavior to achieve legislative goals has intuitive appeal, the broader economic consequences are not always fully understood. In this study, we examine such wider consequences for supply chains when subsidies for CSR are offered. One effect we identify is that since incentives are typically tied to market value, firms have not only an added incentive to achieve societal objectives (say by donating inventory) but also an incentive to raise output (retail) market prices. A second consequence is that since firms forgo potential revenues by engaging in socially desired behavior, they become increasingly sensitive to supplier pricing; in an uncoordinated supply chain this leads to input (wholesale) price concessions. Among other things, the results underscore that incentives put in place to meet broader societal objectives also have notable ramifications for suppliers, retailers, and consumers in primary markets.     

Managing Logistics in Regional Banknote Supply Chain Under Security Concerns

We study the logistics problem faced by Regional Branches (RBs) of a central bank in managing the currency supply under security concerns. While making banknote supply decisions to Sub‐Branches (SBs), the management of RB must achieve two goals simultaneously: (i) guarantee that each SB has sufficient inventories of all denominations of banknotes to satisfy the demands from all commercial banks within its service area, and (ii) control the annual spending on this banknote supply operation. Due to security concerns, the following methods are implemented in the process of transporting banknotes: (i) the capacity of a cash truck is limited by the total face value (instead of the physical space) of banknotes, and (ii) empty decoy trucks are deployed along with the trucks filled with banknotes. After deriving a polynomial‐time strategy to guarantee an optimal solution for the special Bin‐Packing Problem faced in this study, we provide an exact formulation for the RB's supply planning problem. We also propose several polynomial‐time algorithms for deriving either optimal or near‐optimal solutions for the problem under different settings. Using the weekly demand data obtained from the central bank, we verify the performance of our algorithms, and analyze the impacts of changes in these features and in the fleet capacity on the total cost incurred by an RB under various scenarios.     

On some consistent tests of mutual independence among several random vectors of arbitrary dimensions

Statistics and Computing - Testing for mutual independence among several random vectors is a challenging problem, and in recent years, it has gained significant attention in statistics and machine...     

The Impact of Lean Management Practices and Sustainably‐Oriented Innovation on Sustainability Performance of Small and Medium‐Sized Enterprises: Empirical Evidence from the UK

While lean management practices (LMP) help small and medium‐sized enterprises (SMEs) to be efficient, sustainability‐oriented innovation (SOI) facilitates adopting environmental and social practices. Although prior research looks into the effect of LMP on the economic performance (EP) of SMEs, less is known about the effect of LMP on sustainability (economic, environmental and social) performance. Studies on the effect of SOI on sustainability and economic performance are also scant. Additionally, examining the mediating effect of corporate social responsibility (CSR) practices (environmental and social practices) on both LMP and SOI achieving sustainability performance (SP) is rare. This research bridges these knowledge gaps by answering the question of how LMP, SOI, CSR practices, sustainability and economic performance are correlated. Through hypothesis testing using structural equation modelling, this study reveals the impact of LMP, SOI, CSR (environmental and social) practices on sustainability and economic performance. The study uses data from 119 SMEs within manufacturing industries in the Midlands, UK. The analysis reveals that LMP and SOI facilitate achieving both sustainability and economic performance, and SOI mediates LMP to achieve sustainability performance. Additionally, although CSR practices mediate LMP to achieve sustainability performance, they only borderline mediate SOI to achieve sustainability performance.     

Statistical inference based on generalized Lindley record values

This paper addresses the problems of frequentist and Bayesian estimation for the unknown parameters of generalized Lindley distribution based on lower record values. We first derive the exact explicit expressions for the single and product moments of lower record values, and then use these results to compute the means, variances and covariance between two lower record values. We next obtain the maximum likelihood estimators and associated asymptotic confidence intervals. Furthermore, we obtain Bayes estimators under the assumption of gamma priors on both the shape and the scale parameters of the generalized Lindley distribution, and associated the highest posterior density interval estimates. The Bayesian estimation is studied with respect to both symmetric (squared error) and asymmetric (linear-exponential (LINEX)) loss functions. Finally, we compute Bayesian predictive estimates and predictive interval estimates for the future record values. To illustrate the findings, one real data set is analyzed, and Monte Carlo simulations are performed to compare the performances of the proposed methods of estimation and prediction.     

Tempered Mittag-Leffler Lévy processes

In this article, we introduce tempered Mittag-Leffler Lévy processes (TMLLP). TMLLP is represented as tempered stable subordinator delayed by a gamma process. Its probability density function and Lévy density are obtained in terms of infinite series and Mittag-Leffler function, respectively. Asymptotic forms of the tails and moments are given. A step-by-step procedure of the parameters estimation and simulation of sample paths is given. We also provide main results available for Mittag-Leffler Lévy processes (MLLP) and some extensions which are not available in a collective way in a single article. Our results generalize and complement the results available on Mittag-Leffler distribution and MLLP in several directions. Further, the asymptotic forms of the moments of the first-exit times of the TMLLP are also discussed.     

Analyzing the dynamic system model with discrete failure time distribution

The present study deals with the method of estimation of the parameters of k-components load-sharing parallel system model in which each component’s failure time distribution is assumed to be geometric. The maximum likelihood estimates of the load-share parameters with their standard errors are obtained. (1 − γ) 100% joint, Bonferroni simultaneous and two bootstrap confidence intervals for the parameters have been constructed. Further, recognizing the fact that life testing experiments are time consuming, it seems realistic to consider the load-share parameters to be random variable. Therefore, Bayes estimates along with their standard errors of the parameters are obtained by assuming Jeffrey’s invariant and gamma priors for the unknown parameters. Since, Bayes estimators can not be found in closed form expressions, Tierney and Kadane’s approximation method have been used to compute Bayes estimates and standard errors of the parameters. Markov Chain Monte Carlo technique such as Gibbs sampler is also used to obtain Bayes estimates and highest posterior density credible intervals of the load-share parameters. Metropolis–Hastings algorithm is used to generate samples from the posterior distributions of the unknown parameters.