• Communications for Statistical Applications and Methods
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• v.29 no.3
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• pp.373-391
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• 2022

The distribution of observations in most econometric studies with spatial heterogeneity is skewed. Usually, a single transformation of the data is used to approximate normality and to model the transformed data with a normal assumption. This assumption is however not always appropriate due to the fact that panel data often exhibit non-normal characteristics. In this work, the normality assumption is relaxed in spatial mixed models, allowing for spatial heterogeneity. An inference procedure based on Bayesian mixed modeling is carried out with a multivariate skew-elliptical distribution, which includes the skew-t, skew-normal, student-t, and normal distributions as special cases. The methodology is illustrated through a simulation study and according to the empirical literature, we fit our models to non-life insurance consumption observed between 1998 and 2002 across a spatial panel of 103 Italian provinces in order to determine its determinants. Analyzing the posterior distribution of some parameters and comparing various model comparison criteria indicate the proposed model to be superior to conventional ones.

• Communications for Statistical Applications and Methods
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• v.26 no.3
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• pp.315-323
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• 2019

Panel data sets have recently been developed in various areas, and many recent studies have analyzed panel, or longitudinal data sets. Often a dichotomous dependent variable occur in survival analysis, biomedical and epidemiological studies that is analyzed by a generalized linear mixed effects model (GLMM). The most common estimation method for the binary panel data may be the maximum likelihood (ML). Many statistical packages provide ML estimates; however, the estimates are computed from numerically approximated likelihood function. For instance, R packages, pglm (Croissant, 2017) approximate the likelihood function by the Gauss-Hermite quadratures, while Rchoice (Sarrias, Journal of Statistical Software, 74, 1-31, 2016) use a Monte Carlo integration method for the approximation. As a result, it can be observed that different packages give different results because of different numerical computation methods. In this note, we discuss the pros and cons of numerical methods compared with the exact computation method.

• Journal of Energy Engineering
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• v.26 no.2
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• pp.80-92
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• 2017

This paper attempts to estimate the diesel fuel demand function in Korea using panel data panel data of 16 major cities or provinces which consist of diesel demands, diesel market prices and gross value added from the year 1998 to 2015. I apply panel GLS(generalized least square) model, fixed effect model, random effect model and dynamic panel model to estimating the parameters of the diesel fuel demand function. The results show that short-run price elasticities of the diesel fuel demand are estimated to be -0.2146(panel GLS), -0.2886(fixed effect), -0.2854(random effect), -0.1905(dynamic panel) respectively. And short-run income elasticities of the diesel fuel demand are estimated to be 0.7379(panel GLS), 0.4119(fixed effect), 0.7260(random effect), 0.4166(dynamic panel) respectively. The short-run price and income elasticities explain that demand for diesel fuel is price- and income-inelastic. The long-run price and income elasticities are estimated to be -0.4784, 1.0461 by dynamic panel model, which means that demand for diesel fuel is price-inelastic but income-elastic in the long run. In addition I apply dummy variable model to estimate the effect of 16 major cities or provinces on diesel demands. The results show that diesel demands is affected 10 regions on the basis of Seoul.

• Communications for Statistical Applications and Methods
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• v.17 no.3
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• pp.349-356
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• 2010

The ordinary least squares based estimator of the disturbance variance in a regression model for spatial panel data is shown to be asymptotically unbiased and weakly consistent in the context of SAR(1), SMA(1) and SARMA(1,1)-disturbances when there is measurement error in the regressor matrix.

• Health Policy and Management
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• v.29 no.3
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• pp.332-341
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• 2019

Background: This study analyzes the effects of the individual's health behavior on the health and the medical demand for the management of health and medical expenses. Methods: This study uses the Korea Health Panel Survey data from 2010 to 2015. We utilize the panel ordered logit model and the panel Tobit model with the subjective health status and the medical expenses as the dependent variables. Results: Chronic diseases would cause the deterioration of his or her health and the increase in medical expenses. Smoking and drinking alcohol would deteriorate one's health. The total amount of cigarettes increases medical expenses. Exercises could make people healthier, whereas excessive exercise might increase medical expenses. Private health insurance would increase medical expenses. Conclusion: Since health could reduce the medical expenses, people should promote one's health by changing one's behavior for health.

• Journal of the Korean Data and Information Science Society
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• v.19 no.2
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• pp.559-568
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• 2008

This paper analyzes Korean residential tenure choice for house which is the most important in Korean households' assets. Data used in the analysis is the data of Korean Labor and Income Panel Study for the period from 1998 to 2006 and with 2341 households. In this paper, a household chooses a housing tenure mode, either by renting or by owing house. We use a mixed-logit panel model as an estimation model to take into consideration household's heteroscedasticity of preference in tenure choice. It turns out that the heteroscedasticity is significant in households' tenure choice behavior, implying that Korean housing policy emphasizing supply side should consider the demand side.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.4
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• pp.173-183
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• 2022

The objective of this research is to examine the influence of the COVID-19 pandemic on stock markets in a few developing and developed countries. This study uses daily data from January 2020 to May 2021 and obtained from World Health Organization and Thomson Reuters. The secondary data was evaluated through panel econometric methodology that includes different unit root tests, and to analyze the long-run relationship between variables, panel cointegration techniques were applied. The long-run causality among variables was examined through Panel Vector Error Correction Model. The overall findings of this study suggest a long-run association exists between several cases and death with the stock returns of the GCC and other stock markets. Furthermore, the VECM model also identified a long-run causality running from COVID cases and death towards the stock rerun of both sets of stock markets. However, a subsequent Wald test yielded mixed results, indicating no short-run causality between cases and deaths and stock returns in both groups; however, in the case of GCC, several COVID-19 cases are having a causal impact on stock markets, which is notable in light of the fact that the death rate in GCC is significantly lower than in many developed and developing countries.

• The Journal of Korean Association of Computer Education
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• v.17 no.6
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• pp.103-113
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• 2014

This study designed and implemented a query model for consecutive analyses of dynamic multivariate graph data. First, the query model consists of two procedures; setting the discriminant function, and determining an alteration method. Second, the query model was implemented as a query system that consists of a query panel, a graph visualization panel, and a property panel. A Node-Link Diagram and the Force-Directed Graph Drawing algorithm were used for the visualization of the graph. The results of the queries are visually presented through the graph visualization panel. Finally, this study used the data of worldwide import & export data of small arms to verify our model. The significance of this research is in the fact that, through the model which is able to conduct consecutive analyses on dynamic graph data, it helps overcome the limitations of previous models which can only perform discrete analysis on dynamic data. This research is expected to contribute to future studies such as online decision making and complex network analysis, that use dynamic graph models.

• The Korean Journal of Applied Statistics
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• v.25 no.6
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• pp.933-945
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• 2012

In this paper we used the KLIPS(Korean Labor and Income Panel tudy) data that surveyed from 2006(wave 9) to 2009(wave 12). Other previous studies are concerned with the panel attrition in the early wave, but this study classifies the response pattern and investigates some factors that influence panel attrition when the panel tends to stabilize. It was revealed that panel attrition was influenced by relocation and housing type through the logit model. Besides it was appeared that panel attrition was affected by the monthly living expenses and the overall household income through the decision tree.

• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.769-782
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• 2020

Interest in fruit and vegetables has increased due to changes in consumer consumption patterns, socioeconomic status, and family structure. This study determined the factors influencing the demand for fruit and vegetables (strawberries, paprika, tomatoes and cherry tomatoes) using a panel of Rural Development Administration household-level purchases from 2010 to 2018 and compared the ability to the prediction performance. An artificial neural network model was constructed, linking household characteristics with final food expenditure. Comparing the analysis results of the artificial neural network with the results of the panel model showed that the artificial neural network accurately predicted the pattern of the consumer panel data rather than the fixed effect model. In addition, the prediction for strawberries was found to be heavily affected by the number of families, retail places and income, while the prediction for paprika was largely affected by income, age and retail conditions. In the case of the prediction for tomatoes, they were greatly affected by age, income and place of purchase, and the prediction for cherry tomatoes was found to be affected by age, number of families and retail conditions. Therefore, a more accurate analysis of the consumer consumption pattern was possible through the artificial neural network model, which could be used as basic data for decision making.