A polynomial kernel for Block Graph Deletion

In the Block Graph Deletion problem, we are given a graph $G$ on $n$ vertices and a positive integer $k$, and the objective is to check whether it is possible to delete at most $k$ vertices from $G$ to make it a block graph, i.e., a graph in which each block is a clique. In this paper, we obtain a kernel with $\mathcal{O}(k^{6})$ vertices for the Block Graph Deletion problem. This is a first step to investigate polynomial kernels for deletion problems into non-trivial classes of graphs of bounded rank-width, but unbounded tree-width. Our result also implies that Chordal Vertex Deletion admits a polynomial-size kernel on diamond-free graphs. For the kernelization and its analysis, we introduce the notion of `complete degree' of a vertex. We believe that the underlying idea can be potentially applied to other problems. We also prove that the Block Graph Deletion problem can be solved in time $10^{k}\cdot n^{\mathcal{O}(1)}$.Comment: 22 pages, 2 figures, An extended abstract appeared in IPEC201

An Alternative System GMM Estimation in Dynamic Panel Models

The system GMM estimator in dynamic panel data models which combines two moment conditions, i.e., for the differenced equation and for the model in levels, is known to be more efficient than the first-difference GMM estimator. However, an initial optimal weight matrix is not known for the system estimation procedure. Therefore, we suggest the use of 'a suboptimal weight matrix' which may reduce the finite sample bias whilst increasing its efficiency. Using the Kantorovich inequality, we find that the potential efficiency gain becomes large when the variance of individual effects increases compared to the variance of the idiosyncratic errors. (Our Monte Carlo experiments show that the small sample properties of the suboptimal system estimator are shown to be much more reliable than any other conventional system GMM estimator in terms of bias and efficiency.Dynamic panel data, sub-optimal weighting matrix, KI upper boud

A Typology and Life Satisfaction of Older Koreans: A Longitudinal Comparison

Aging is a global phenomenon for many countries and Korea is not an exception. After becoming an aging society in 2000, Korea turned an aged country in 2017 by having 14.3% of its population with older than 65 years old. It is expected to become a super-aged society by 2025 (Statistics Korea 2018). No other country in the world has aged this fast. Unlike other developed countries that had a lot more time to deal with the aged population, Korean has to deal with the aging population without much preparation time. In this fast transition, knowing who they are and how they transform as aging progresses is important for both policymakers and businessmen. The objective of this study is to identify different segments of older Koreans based on their value system and to make a longitudinal comparison by using survey data collected in 2009 and 2017

The effect of a market factor on information flow between stocks using minimal spanning tree

We empirically investigated the effects of market factors on the information flow created from N(N-1)/2 linkage relationships among stocks. We also examined the possibility of employing the minimal spanning tree (MST) method, which is capable of reducing the number of links to N-1. We determined that market factors carry important information value regarding information flow among stocks. Moreover, the information flow among stocks evidenced time-varying properties according to the changes in market status. In particular, we noted that the information flow increased dramatically during periods of market crises. Finally, we confirmed, via the MST method, that the information flow among stocks could be assessed effectively with the reduced linkage relationships among all links between stocks from the perspective of the overall market