AN ANALYSIS OF EQUILIBRIUM RELATIONSHIP BETWEEN PRICE ELASTICITY AND EXPENDITURE LEVEL: A CASE STUDY OF KOREAN MOBILE MARKET DATA

In most developing countries, telecommunications industry has been grown fast and still has more growth potential than in the developed countries. Clearly the telecommunications industry contributes to foster economic developments and also to narrow the communication gaps among countries. Among many components relating to the success of quick developments of telecommunication services, an appropriate and optimal pricing strategies is the most vital element. In this view point, this paper examines the optimal price discrimination strategy for firms in a monopolistically competitive market. The primary interest is the theoretical relationship between price elasticity and the average expenditure level of consumers. Our equilibrium analysis shows that the relationship can go either way (positive or negative) depending on the prevailing price level of the product in concern. As an empirical example, using a hierarchical Bayes model we find that heavy user of mobile service are substantially more elastic to the price of calls in Korea. A discussion of the optimal pricing scheme and market structure is in order.Price Discrimination, Price Elasticity, Price Sensitivity, Mobile Telecommunications, Hierarchical Bayes Model

DDP-GCN: Multi-Graph Convolutional Network for Spatiotemporal Traffic Forecasting

Traffic speed forecasting is one of the core problems in Intelligent Transportation Systems. For a more accurate prediction, recent studies started using not only the temporal speed patterns but also the spatial information on the road network through the graph convolutional networks. Even though the road network is highly complex due to its non-Euclidean and directional characteristics, previous approaches mainly focus on modeling the spatial dependencies only with the distance. In this paper, we identify two essential spatial dependencies in traffic forecasting in addition to distance, direction and positional relationship, for designing basic graph elements as the smallest building blocks. Using the building blocks, we suggest DDP-GCN (Distance, Direction, and Positional relationship Graph Convolutional Network) to incorporate the three spatial relationships into prediction network for traffic forecasting. We evaluate the proposed model with two large-scale real-world datasets, and find 7.40% average improvement for 1-hour forecasting in highly complex urban networks

A Characterization of Optimal Feasible Tax Mechanism

This paper is motivated by a practical income (or wealth) taxation problem: For a public good economy where the provision of public goods is to be financed by income taxes collected from individuals, what is the optimal feasible tax mechanism when a social planner is relatively uninformed of the incomes or endowments of the individuals? This kind of problem, the optimal private provision of public goods, is a typical Bayesian mechanism design question for a small economy such as a club. In this case, the social planner has to take into account not only the individuals' incentive to report their income truthfully, but also the (individual) feasibility of the designed tax mechanism in the sense that each individual's tax payment should be consistent with their ability to pay. We employ the feasible implementation model used in Hurwicz, Maskin, and Postlewaite [1995] to study such an optimal taxation problem. It has been assumed in the standard model of optimal labor income taxation literature, pioneered by Mirrlees [1971], that there is a continuum of individuals and the (labor) income is observable to avoid the feasibility problem. Also, the literature on private provision of public goods has paid little attention to the continuous provision of public goods and the constrained efficiency under incomplete information. This paper considers a finite economy where public goods are provided continuously. Using a simple Bayesian model, we provide the full characterization of the two-agent, two-type optimal feasible tax mechanism and its properties. We find that (i) when the total endowment of the economy is relatively low enough or high enough, the first best feasible taxation can be obtained; (ii) the second best feasible tax mechanism requires a poor agent to pay relatively more than a rich agent, that is, it is regressive; and (iii) the tax mechanism is increasing in the sense that the agent's tax payment increases with his endowment. We also provide a comparative statics analysis. For the case of more than two agents, under certain mild assumptions we give some partial results similar to (i) and (ii) above. In addition, we find the optimal feasible tax mechanism for the corresponding infinitely large economyoptimal taxation, feasibility, informational rent, second best

HABIT FORMATION AND PRECAUTIONARY SAVING: EVIDENCE FROM THE KOREAN HOUSEHOLD PANEL STUDIES

We examined the evidence of precautionary saving and Constantinides (1990) type habit formation model by using the Korean Household Panel Studies, which has six years of time series observations and thousands of cross section observations. Employing the dynamic panel data estimation method, we found that the estimates of parameters corresponding to the degree of habit formation and precautionary saving are statistically insignificant for food consumption, but statistically significant or at least marginally significant for nondurables and services consumption.Habit Formation, Precautionary Saving, Korean Household Panel Studies, Dynamic Panel Data Estimation

Utilizing Class Information for Deep Network Representation Shaping

Statistical characteristics of deep network representations, such as sparsity and correlation, are known to be relevant to the performance and interpretability of deep learning. When a statistical characteristic is desired, often an adequate regularizer can be designed and applied during the training phase. Typically, such a regularizer aims to manipulate a statistical characteristic over all classes together. For classification tasks, however, it might be advantageous to enforce the desired characteristic per class such that different classes can be better distinguished. Motivated by the idea, we design two class-wise regularizers that explicitly utilize class information: class-wise Covariance Regularizer (cw-CR) and class-wise Variance Regularizer (cw-VR). cw-CR targets to reduce the covariance of representations calculated from the same class samples for encouraging feature independence. cw-VR is similar, but variance instead of covariance is targeted to improve feature compactness. For the sake of completeness, their counterparts without using class information, Covariance Regularizer (CR) and Variance Regularizer (VR), are considered together. The four regularizers are conceptually simple and computationally very efficient, and the visualization shows that the regularizers indeed perform distinct representation shaping. In terms of classification performance, significant improvements over the baseline and L1/L2 weight regularization methods were found for 21 out of 22 tasks over popular benchmark datasets. In particular, cw-VR achieved the best performance for 13 tasks including ResNet-32/110.Comment: Published in AAAI 201

Further Reforms after the "BIG BANG": The Japanese Government Bond Market

This paper identifies key steps for further development of the JGB market in aligning its infrastructures with those of the U.S. and U.K. government securities markets. One major impediment to the JGB market development is commingled management of government assets and liabilities. Especially Fiscal Investment and Loan Program's inadvertent influence over monetary policy not only causes the cost of government-issued debt to increase but also creates serious impediments to the development of the JGB markets. Therefore, it is recommended that Ministry of Finance's involvement in the JGB market should be limited to issuer's function in the capacity of government debt manager and a "hands-off" policy be adopted by the Ministry of Finance to give all FILP agencies complete autonomy. Additional reform measures are recommended to create a more efficient and effective JGB market: (i) promote JGBs with non-resident investors; (ii) introduce the primary dealer system; (iii) adopt the uniform-price auction method; (iv) allow when-issued trading; (v) develop a truly American-style REPO market; and (vi) introduce STRIPS.

Hybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classification

Network biology has been successfully used to help reveal complex mechanisms of disease, especially cancer. On the other hand, network biology requires in-depth knowledge to construct disease-specific networks, but our current knowledge is very limited even with the recent advances in human cancer biology. Deep learning has shown a great potential to address the difficult situation like this. However, deep learning technologies conventionally use grid-like structured data, thus application of deep learning technologies to the classification of human disease subtypes is yet to be explored. Recently, graph based deep learning techniques have emerged, which becomes an opportunity to leverage analyses in network biology. In this paper, we proposed a hybrid model, which integrates two key components 1) graph convolution neural network (graph CNN) and 2) relation network (RN). We utilize graph CNN as a component to learn expression patterns of cooperative gene community, and RN as a component to learn associations between learned patterns. The proposed model is applied to the PAM50 breast cancer subtype classification task, the standard breast cancer subtype classification of clinical utility. In experiments of both subtype classification and patient survival analysis, our proposed method achieved significantly better performances than existing methods. We believe that this work is an important starting point to realize the upcoming personalized medicine.Comment: 8 pages, To be published in proceeding of IJCAI 201

Alignment of cD-galaxies with their surroundings

For a sample of 122 rich Abell clusters the authors find a strong correlation of the position angle (orientation) of the first-ranked galaxy and its parent cluster. This alignment effect is strongest for cD-galaxies. Formation scenarios for cD galaxies, like the merging scenario, must produce such a strong alignment effect. The authors show some N-body simulations done for this purpose