Analyzing the Impact of Macroeconomic Shocks on Public Debt Dynamics: An Application to the Czech Republic

The global financial crisis and its ramification into the fiscal area have demonstrated the importance of regular assessment and monitoring of fiscal vulnerabilities, including the sustainability of sovereign debt. This paper extends the analytical framework of Favero and Giavazzi (2007) to facilitate the analysis of the effects of macroeconomic shocks on public debt dynamics in an open economy. It then applies this framework using the data for the Czech Republic and derives some policy implications from such an analysis. The modeling framework nests a linear structural vector auto-regression (SVAR) model estimated with short-run identifying restrictions and a non-linear equation describing the public debt dynamics. The main variables of the system include GDP growth, inflation, the effective interest rate on government debt, government expenditures and revenues, the exchange rate and government debt. The utilized estimation method is the Bayesian approach.Macroeconomic Shocks, Non-linear Public Debt Dynamics, Open Economy, Czech Republic, Structural Vector Autoregression Model, Bayesian Estimation

USING PROBABILISTIC GRAPHICAL MODELS TO DRAW INFERENCES IN SENSOR NETWORKS WITH TRACKING APPLICATIONS

Sensor networks have been an active research area in the past decade due to the variety of their applications. Many research studies have been conducted to solve the problems underlying the middleware services of sensor networks, such as self-deployment, self-localization, and synchronization. With the provided middleware services, sensor networks have grown into a mature technology to be used as a detection and surveillance paradigm for many real-world applications. The individual sensors are small in size. Thus, they can be deployed in areas with limited space to make unobstructed measurements in locations where the traditional centralized systems would have trouble to reach. However, there are a few physical limitations to sensor networks, which can prevent sensors from performing at their maximum potential. Individual sensors have limited power supply, the wireless band can get very cluttered when multiple sensors try to transmit at the same time. Furthermore, the individual sensors have limited communication range, so the network may not have a 1-hop communication topology and routing can be a problem in many cases. Carefully designed algorithms can alleviate the physical limitations of sensor networks, and allow them to be utilized to their full potential. Graphical models are an intuitive choice for designing sensor network algorithms. This thesis focuses on a classic application in sensor networks, detecting and tracking of targets. It develops feasible inference techniques for sensor networks using statistical graphical model inference, binary sensor detection, events isolation and dynamic clustering. The main strategy is to use only binary data for rough global inferences, and then dynamically form small scale clusters around the target for detailed computations. This framework is then extended to network topology manipulation, so that the framework developed can be applied to tracking in different network topology settings. Finally the system was tested in both simulation and real-world environments. The simulations were performed on various network topologies, from regularly distributed networks to randomly distributed networks. The results show that the algorithm performs well in randomly distributed networks, and hence requires minimum deployment effort. The experiments were carried out in both corridor and open space settings. A in-home falling detection system was simulated with real-world settings, it was setup with 30 bumblebee radars and 30 ultrasonic sensors driven by TI EZ430-RF2500 boards scanning a typical 800 sqft apartment. Bumblebee radars are calibrated to detect the falling of human body, and the two-tier tracking algorithm is used on the ultrasonic sensors to track the location of the elderly people

Advanced economies and emerging markets: Dissecting the drivers of business cycle synchronization

What are the divers of business cycle synchronization within and between advanced and emerging economies at the sector level? This question is addressed by analysing international co-movements of value added growth in a multi-sector dynamic factor model. The model contains a world factor, region factors, sector factors, country factors, and idiosyncratic components. The model is estimated using Bayesian methods for 9 disaggregated sectors in 5 developed economies (G5) and 19 emerging economies for the 1972-2009 period. The results suggest that, while there exists a common ‘regional business cycle’ in the G5, fluctuations in sectoral value added growth are dominated by country-specific factors in the emerging markets. Despite that, the international factor (the sum of world and sector factors) is more important than the region factor, suggesting that the emerging markets are more synchronized with the G5. A simple regression shows that (i) the world factor would be more important the larger the share of agriculture in output; (ii) in more open economies the sector factor is more important in explaining sectoral VA growth fluctuations; (iii) the region factors is more important the richer and the less volatile the economy. Finally, a comparison of the variance of sectoral value added growth accounted for by each factor from the pre- to the post-globalization period shows convergence of the business cycles within the G5 and EM, respectively. The changes in the contribution of the world, sector and region factor are due to changes in the importance of those factors within sectors. However, for the emerging markets, the fall in the importance of the country factors is dominated by changes in the structural composition of the economies. Therefore, the evolution of the structural composition in the emerging markets could be an important driver for more synchronized business cycles at the regional and international level

A Formal Framework for Modeling Trust and Reputation in Collective Adaptive Systems

Trust and reputation models for distributed, collaborative systems have been studied and applied in several domains, in order to stimulate cooperation while preventing selfish and malicious behaviors. Nonetheless, such models have received less attention in the process of specifying and analyzing formally the functionalities of the systems mentioned above. The objective of this paper is to define a process algebraic framework for the modeling of systems that use (i) trust and reputation to govern the interactions among nodes, and (ii) communication models characterized by a high level of adaptiveness and flexibility. Hence, we propose a formalism for verifying, through model checking techniques, the robustness of these systems with respect to the typical attacks conducted against webs of trust.Comment: In Proceedings FORECAST 2016, arXiv:1607.0200

Smart Steaming: A New Flexible Paradigm for Synchromodal Logistics

Slow steaming, i.e., the possibility to ship vessels at a significantly slower speed than their nominal one, has been widely studied and implemented to improve the sustainability of long-haul supply chains. However, to create an efficient symbiosis with the paradigm of synchromodality, an evolution of slow steaming called smart steaming is introduced. Smart steaming is about defining a medium speed execution of shipping movements and the real-time adjustment (acceleration and deceleration) of traveling speeds to pursue the entire logistic system’s overall efficiency and sustainability. For instance, congestion in handling facilities (intermodal hubs, ports, and rail stations) is often caused by the common wish to arrive as soon as possible. Therefore, smart steaming would help avoid bottlenecks, allowing better synchronization and decreasing waiting time at ports or handling facilities. This work aims to discuss the strict relationships between smart steaming and synchromodality and show the potential impact of moving from slow steaming to smart steaming in terms of sustainability and efficiency. Moreover, we will propose an analysis considering the pros, cons, opportunities, and risks of managing operations under this new policy

International Capital Flows and Boom-Bust Cycles in the Asia Pacific Region

This paper documents evidence of business cycle synchronization in selected Asia Pacific countries in the 1990s. We explain business cycle synchronization by the channel of international capital flows. Using the VAR method, we find that most Asian countries experience boom-bust cycles following capital inflows, where the boom in output is mostly driven by consumption and investment. Empirical evidence shows that capital flows in the region are highly correlated, which supports the conclusion that capital market liberalization has contributed to business cycle synchronization in Asia. We also find that business cycles in the Asian crisis countries are highly synchronized with those in Japan.business cycle synchronization, capital flows, boom-bust cycles, financial integration