Characterizations of negative multinomial distributions based on conditional distributions

In this paper characterizations of negative multinomial distributions based on conditional distributions have been studied.     

Sub additive measures of relative information and inaccuracy

Summary In the study of information theoretic measures, additivity has been the basic requirement. However it is quite interesting to investigate the sub-additive measures. Starting from subadditivity for measures associated with a pair of distributions of a discrete random variable, it has been changed into an equality relation using another function of a pair of distributions., Under the sum property of the function and the measures, the relation is expressed in terms of a functional equation of which the most general complex solutions have been obtained. In terms of the real continuous solutions of the functional equation, the sub-additive measures of Relative information and Inaccuracy have been defined and characterized. Particular cases and simple properties particularly the convexities of some of these new measures have also been studied.     

Natural resource accounting for Indian states — Illustrating the case of forest resources

Our main objective is to set out and apply a SEEA-based methodology to reflect the true value of forest resources in India's national and state accounts. We establish that a “top-down” approach using available national databases is both feasible and desirable from a policy perspective. In this paper, we address four components of value creation in forests: timber production, carbon storage, fuelwood usage, and the harvesting of non-timber forest products. The results of our analysis suggest that prevailing measures of national income in India underestimate the contribution of forests to income. The income accounts of the Northeastern states in particular are significantly understated by these traditional (GDP/GSDP) measures. We are also able to identify some states which performed poorly in the context of our sustainability framework, reflecting natural capital losses due to degradation and deforestation. Our results highlight the need to integrate natural resource accounting into the national accounting framework in order to generate appropriate signals for sustainable forest management and for the conservation of forest resources which are widely used by the poor in India, as well as being significant stores of national wealth.     

The effectiveness of government spending on education and health care in developing and transition economies

Recent studies show that corruption is associated with higher military spending [Eur. J. Polit. Econ. 17 (2001) 794] and lower government spending on education and health care [J. Publ. Econ. 69 (1998) 263]. This suggests that policies aimed at reducing corruption may lead to changes in the composition of government outlays toward more productive spending. However, little empirical evidence has been presented to support the claim that public spending improves education and health indicators in developing and transition countries. This paper uses cross-sectional data for 50 such countries to show that increased public expenditure on education and health care is associated with improvements in both access to and attainment in schools, and reduces mortality rates for infants and children. The education regressions are robust to different specifications, but the relationship between health care spending and mortality rates is weaker.     

Structural holes in social networks

We consider a setting where every pair of players that undertake a transaction (e.g. exchange goods or information) creates a unit surplus. A transaction can take place only if the players involved have a connection. If the connection is direct the two players split the surplus equally, while if it is indirect then intermediate players also get an equal share of the surplus. Thus, individuals form links with others to create surplus, to gain intermediation rents, and to circumvent others who are trying to become intermediary.Our analysis clarifies the interplay between these forces in the process of strategic network formation. First, we show that, in the absence of capacity constraints on links, it leads to the emergence of a star network where a single agent acts as an intermediary for all transactions and enjoys significantly higher payoffs. Second, we study the implications of capacity constraints in the ability of agents to form links. In this case, distances between players must be long, which induces players who are “far off” to connect in order to avoid paying large intermediation rents. A cycle network then emerges, payoffs being equal across all players.     

A non-linear approach for predicting stock returns and volatility with the use of investor sentiment indices

The popular sentiment-based investor index S^BW introduced by Baker and Wurgler (2006, 2007) is shown to have no predictive ability for stock returns. However, Huang et al. (2015) developed a new investor sentiment index, S^PLS, which can predict monthly stock returns based on a linear framework. However, the linear model may lead to misspecification and lack of robustness. We provide statistical evidence that the relationship between stock returns, S^BW and S^PLS is characterized by structural instability and inherent nonlinearity. Given this, using a nonparametric causality approach, we show that neither S^BW nor S^PLS predicts stock market returns or even its volatility, as opposed to previous empirical evidence.     

Economic integration and stock market dynamic linkages: evidence in the context of Australia and Asia

Countries are becoming economically integrated and it is contended that this will also lead to their financial markets becoming integrated. This contention is important since international financial market integration diminishes portfolio diversification benefits and creates contagion risk. We test this contention in this article in the context of the Australasian region. Australia and Asia have experienced very significant economic integration through a rapid growth in their bilateral trade. We utilize a battery of econometric techniques – cointegration, asymmetric generalized dynamic conditional correlations and panel regression models. As expected, we find that trade intensity significantly drives the interdependence between their stock markets in both the short run and the long run. Thus, given the ever increasing economic integration in this region, this finding implies that their stock markets face the risk of contagion, and that investors in these markets would also be confronted with the prospect of lower diversification benefits.     

The causal relationship between natural gas consumption and economic growth: evidence from the G7 countries

This article re-examines the nature of the causality between natural gas consumption and economic growth in G7 countries over the period from 1965 to 2011. We employ the Granger causality procedure proposed by Emirmahmutoglu and Kose (2011) which takes into account cross-sectional dependency and heterogeneity across countries. Our overall empirical results support the neutrality hypothesis for the panel while the individual country results confirm the same result with the exception of the case of UK, where the conservation hypothesis is confirmed, showing that GDP causes natural gas consumption in the country. These results make policies that promote the consumption of natural gas risk-free with regard to their effects to the economic growth and development levels.     

Forecasting US consumer price index: does nonlinearity matter?

The objective of this article is to predict, both in sample and out of sample, the consumer price index (CPI) of the US economy based on monthly data covering the period of 1980:1–2013:12, using a variety of linear (random walk (RW), autoregressive (AR) and seasonal autoregressive integrated moving average (SARIMA)) and nonlinear (artificial neural network (ANN) and genetic programming (GP)) univariate models. Our results show that, while the SARIMA model is superior relative to other linear and nonlinear models, as it tends to produce smaller forecast errors; statistically, these forecasting gains are not significant relative to higher-order AR and nonlinear models, though simple benchmarks like the RW and AR(1) models are statistically outperformed. Overall, we show that in terms of forecasting the US CPI, accounting for nonlinearity does not necessarily provide us with any statistical gains.