Fu Jen Catholic University

About: Fu Jen Catholic University is a education organization based out in Taipei, Taiwan. It is known for research contribution in the topics: Population & Hazard ratio. The organization has 6842 authors who have published 9512 publications receiving 171005 citations. The organization is also known as: FJU & Fu Jen.

The Fourier approximation and testing for the null of cointegration

Abstract: In this paper, we propose a test to investigate the null of cointegration allowing for structural breaks of unknown form in deterministic trend by using the Fourier form. The test is developed on the basis of the fact that structural breaks of unknown form can be approximated with a low-frequency Fourier component. As a result, the statistic is able to test cointegration without estimating specific break dates. The asymptotic distribution of the test is derived, and the asymptotic critical values are tabulated. Simulation experiments show that the test can deliver robust type I error for various breaks commonly seen in economic analysis and have good power, even in small sample sizes encountered in empirical studies. Our test is applied to analyze the issue of fiscal sustainability in the nine OECD countries with a high debt-to-GDP ratio.

Internal liquidity risk in corporate bond yield spreads

Abstract: The recent global financial crisis reveals the important role of internal liquidity risk in corporate credit risk. However, few existing studies investigate its effects on bond yield spreads. Panel data for the period from year 1993 through 2008 show that corporate internal liquidity risk significantly impacts bond yield spreads (and changes) when controlling for well-known bond yield determinant variables, traditional accounting measures of corporate debt servicing ability, cash flow volatility, credit ratings, and state variables. This finding indicates that internal liquidity risk should therefore be incorporated into bond yield spread modeling.

Efficient Fault-Tolerant Routing in IoT Wireless Sensor Networks Based on Bipartite-Flow Graph Modeling

Abstract: In the Internet of Things (IoT), a wireless sensor network (WSN) is deployed for collecting the interesting data of an application field. Sensor nodes in an IoT WSN are usually with the heterogeneous property. Some nodes have more power (energy) and additional functionality (e.g., data aggregation) than others. Cluster-based routing is usually used in WSNs for data transmissions due to efficiently routing consideration. Based on cluster-based routing, the cluster heads (CHs) act as the sensed data forwarding role. Once one or more CHs fail, the faulty CHs cannot forward the sensed data of their serving sensor nodes. As a result, the sink node (gateway) has not sufficient sensed data of the IoT application field. This will deeply affect the information processing of the IoT applications. We utilize the virtual CH formation and flow graph modeling to efficiently tolerate the failures of CHs. First, the available resources of all failure-free CHs are logically organized as a virtual CH to be the common backup of all faulty CHs. Then, the flow graph modeling is used to achieve fault tolerance with the minimum total energy consumption among all failure-free CHs. Finally, we perform extensive experiments to demonstrate the effectiveness of our approach in the fault-tolerant routing of the IoT WSNs.