Demand Driven Growth in Small Open and Import Dependable Economy

Demand driven growth is rather a common approach in many countries in the short run. Growth in aggregate demand pushes production to higher level, increasing employment and income. But what is the case in small open economies, which are highly import dependable, service-oriented, and have to import most consumers＇ goods？ Research is focused on the case of small open economy （Montenegro）. Research will be based on statistical data for Montenegro, for period from 2000 to 2011. Data are processed in Eviews, using Least Square method to estimate equations and models. Research has shown that gross domestic product （GDP） growth in the short run, prior to global financial crisis, was achieved through growth in consumption and investment, which led to growth in import and growth in foreign debt, as consumption was financed significantly borrowing foreign financial resources. After the crisis, financial inflows dropped, leaving Montenegrin economy to struggle with increased debt （both public and private）, unfinished investment project to provide value added and low level of domestic production leading to even higher trade deficit. Future growth can be achieved only if it is driven by investments, as growth in consumption will more likely lead to higher trade deficit than production growth.

Spillover and quantile linkage between oil price shocks and stock returns: new evidence from G7 countries

The link between crude oil price and stock returns of the Group of Seven(G7)countries(Canada,France,Germany,Italy,Japan,the United Kingdom,and the United States)was analyzed in this study using monthly data from January 1999 to March 2020.We adopt a similar approach to Kilian(Am Econ Rev 99(3):1053–1069,2009)and construct a structural vector autoregression framework to decompose crude oil price shocks into oil supply shock,oil aggregate demand shock,and oil-specific demand shock.We then explore the distinct effects of different kinds of oil price shocks from various sources.Based on the decomposed oil price shocks,we apply the connectedness approach and QQ regression to find time-varying co-movements and tail dependence between oil price shocks and G7 stock returns.There is no general correlation between the decomposed oil prices and stock returns in these countries.The effects of oil price shocks on stock returns across different stock market conditions appear to be heterogeneous.Oil supply shock appears to be a net transmitter of spillover effects for all G7 countries within the sample period.

Variability scaling and capacity planning in Covid-19 pandemic

Capacity planning is a very important global challenge in the face of Covid-19 pandemic.In order to hedge against the fluctuations in the random demand and to take advantage of risk pooling effect,one needs to have a good understanding of the variabilities in the demand of resources.However,Covid-19 predictive models that are widely used in capacity planning typically often predict the mean values of the demands(often through the predictions of the mean values of the confirmed cases and deaths)in both the temporal and spatial dimensions.They seldom provide trustworthy prediction or estimation of demand variabilities,and therefore,are insufficient for proper capacity planning.Motivated by the literature on variability scaling in the areas of physics and biology,we discovered that in the Covid-19 pandemic,both the confirmed cases and deaths exhibit a common variability scaling law between the average of the demand μ and its standard deviationσ,that is,σ ∝ μ^(β),where the scaling parameterμis typically in the range of 0.65 to 1,and the scaling law exists in both the temporal and spatial dimensions.Based on the mechanism of contagious diseases,we further build a stylized network model to explain the variability scaling phenomena.We finally provide simple models that may be used for capacity planning in both temporal and spatial dimensions,with only the predicted mean demand values from typical Covid-19 predictive models and the standard deviations of the demands derived from the variability scaling law.

A Programming Language Approach to Internet-Based Virtual Computing Environment

There is an increasing need to build scalable distributed systems over the Internet infrastructure. However the development of distributed scalable applications suffers from lack of a wide accepted virtual computing environment. Users have to take great efforts on the management and sharing of the involved resources over Internet, whose characteristics are intrinsic growth, autonomy and diversity. To deal with this challenge, Internet-based Virtual Computing Environment （iVCE） is proposed and developed to serve as a platform for distributed scalable applications over the open infrastructure, whose kernel mechanisms are on-demand aggregation and autonomic collaboration of resources. In this paper, we present a programming language for iVCE named Owlet. Owlet conforms with the conceptual model of iVCE, and exposes the iVCE to application developers. As an interaction language based on peer-to-peer content-based publish/subscribe scheme, Owlet abstracts the Internet as an environment for the roles to interact, and uses roles to build a relatively stable view of resources for the on-demand resource aggregation. It provides language constructs to use 1） distributed event driven rules to describe interaction protocols among different roles, 2） conversations to correlate events and rules into a common context, and 3） resource pooling to do fault tolerance and load balancing among networked nodes. We have implemented an Owlet compiler and its runtime environment according to the architecture of iVCE, and built several Owlet applications, including a peer-to-peer file sharing application. Experimental results show that, with iVCE, the separation of resource aggregation logic and business logic significantly eases the process of building scalable distributed applications.