• International Commerce and Information Review
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• v.12 no.4
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• pp.151-170
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• 2010

The present study made an empirical analysis for investigating the competitiveness of technology trades in Korea. In particular, the study deduced the correlation between technology export and technology import using the variables of Gross Domestic Expenditure on R&D and Per capita industry value added Productivity and employed fixed effect model in panel linear regression model. It is found that the R&D expenditure of OECD countries made a significant effect on the technology import and the value-added labor productivity made a significant result on both technology export and import. Therefore, it showed that the technology trade in Korea made a sensitive response to labor productivity in OECD countries. By panel analysis, machine, construction, ICT, and service industry affect most on technology export in Korea for recent 5 years. For technology import, electric-electron, chemical, service, and construction industry have significant effects. This study contributed to understanding of industrial characteristics affecting technology trades in Korea and empirical analysis to show correlation between the factors affecting technology trade.

• Journal of the Korean Statistical Society
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• v.30 no.3
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• pp.511-527
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• 2001

This paper consider the testing problem of variance component for the unbalanced tow=-way error component model. We provide a conditional LM test statistic for testing zero individual(time) effects assuming that the other time-specific(individual)efefcts are present. This test is extension of Baltagi, Chang and Li(1998, 1992). Monte Carlo experiments are conducted to study the performance of this LM test.

• The Korean Journal of Applied Statistics
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• v.34 no.5
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• pp.697-710
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• 2021

Relative error prediction is preferred over ordinary prediction methods when relative/percentile errors are regarded as important, especially in econometrics, software engineering and government official statistics. The relative error prediction techniques have been developed in linear/nonlinear regression, nonparametric regression using kernel regression smoother, and stationary time series models. However, random effect models have not been used in relative error prediction. The purpose of this article is to extend relative error prediction to some of generalized linear mixed model (GLMM) with panel data, which is the random effect models based on gamma, lognormal, or inverse gaussian distribution. For better understanding, the real auto insurance data is used to predict the claim size, and the best predictor and the best relative error predictor are comparatively illustrated.

• Journal of the Korean Statistical Society
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• v.29 no.1
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• pp.63-80
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• 2000

We consider the panel data regression model with nested error componets. In this paper, the several Lagrange Multipler tests for the nested error component model are derived. These tests extend the earlier work of Honda(1985), Moulton and Randolph(1989), Baltagi, et al.(1992) and King and Wu(1997) to the nested error component case. Monte Carlo experiments are conducted to study the performance of these LM tests.

• Journal of Environmental Policy
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• v.3 no.2
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• pp.89-112
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• 2004

This Study aims to demonstrate how much flood vulnerability in urban area changed for the past 32 years by using the panel model. At the same time, this study strives to determine the primary factors and to construct an effective counter-plan by means of empirical research. After selecting research hypotheses based on considerations of issues concerning causes for urban flooding, their relevance is put to the test by conducting empirical research in individual case locations. This research verifies the four research hypotheses by using one-way error component regression model. In conclusion, this research has shown that urban land use and local characteristics act as significant flood determinants, with forests acting to reduce flood dangers. Moreover, constructing embankments can no longer represent a reliable flood control policy. The changes in future flood control policies need to incorporate local characteristics and to minimize natural destruction, so that humans and nature can coexist through environmentally friendly flood management policies.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.1
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• pp.46-56
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• 2018

This paper aimed to identify main factors of community characters, which have an effect on the land surface temperature(LST) change and estimate the impacting coefficient(ratio) of factors in a significant level of statistics. Chungcheongbuk-do province was selected and then partitioned into city and county areas for the sake of convenience of modeling. LST time series data and the community character data were developed based on Terra Satellite MODIS data and collected from the National Statistical Office, respectively. By the cause and effect relationship between community characters and LST, regression coefficients were estimated using a penal model. In a panel modeling, LST and community characters were used as a dependent variable and explanatory variables, respectively. Panel modeling analysis was carried out using statistical package STATA14 and one-way fixed effect model was selected as the most suitable model to evaluate the regression coefficients in the study area. The impacting ratio of LST change by any explanatory variable derived from the regression coefficients of the panel model fixed. Impacting ratios for industrial areas, elevation ${\times}$ building, energy usage, average window speed, non-urban management area, agricultural, nature and environmental conservation, average precipitation were 3.746, 2.856, 2.742, 0.553, 0.102, 0.071 and 0.003, respectively.

• The Journal of Fisheries Business Administration
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• v.51 no.1
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• pp.81-96
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• 2020

The purpose of this paper is to analyze factors affecting the net income of regional and industrial fisheries cooperatives in South Korea using panel data. This paper utilizes linear or GLS regression models such as pooled OLS model, fixed effects model, and random effects model to estimate affecting factors of the net income of regional and industrial fisheries cooperatives. After reviewing various tests, we eventually select random effects model. The results, based on panel data between 2013 and 2018 year and 64 fisheries cooperatives, indicate that capital and area dummy variables have positive effects and employment has negative effect on the net income of regional and industrial fisheries cooperatives as predicted. However, debt are opposite with our predictions. Specifically, it turns out that debt has positive effect on the net income of regional and industrial fisheries cooperatives although it has been increased. Additionally, this paper shows that the member of confreres does not show any significant effect on the net income of regional and industrial fisheries cooperatives in South Korea. This study is significant in that it analyzes the major factors influencing changes in the net income that have not been conducted recently for the fisheries cooperatives by region and industry.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.7
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• pp.139-147
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• 2020

This paper aims to explore the relationship between the quality of the audit and the level of stock return co-movement in the context of the Vietnamese emerging market. The empirical study is designed based on the quatitative method and deductive approach. The panel dataset includes 256 listed firms from different industries,with 1115 firm-year observations on Ho Chi Minh City Stock Exchange for the period from 2014 to 2018. In the research, we built the econometric regression model, using stock return synchronicity and audit quality as the dependent and independent variable, respectively. Some control variables are also added to the econometric regression models as they are well-documented in prior research to have an effect on stock price synchronicity. To improve the accuracy of the regression coefficients, beside the Ordinary Least Squares, we employ the Random Effects Model and the Fixed Effects Model for better statistical analysis of panel data set. The results show that the quality of the audit is positively correlated to stock price synchronicity. This finding suggests that stock returns of companies with higher quality of the audit are more synchronous with the market. Results for other control variables also support our reasoning for the main findings.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.9
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• pp.181-189
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• 2022

The current study examines the nexus between the capital structure (debt-equity) and firm value (Tobin's Q) by including profitability (alternatively Return on Assets (ROA) and Return on Equity (ROE)) as a moderator in the companies of Saudi Arabia. The study sample consists of 102 companies listed on Tadawul (the Saudi Arabian stock exchange) from different sectors of Saudi Arabia during the period 2013 to 2020. The study estimates pooled regression, panel regression with fixed and random effects, and dynamic panel regression models to report the results. The study results report that there is a negative and significant association between capital structure and firm value in model 1, while in models 2 and 3 there is a more negative and significant impact between the two study variables compared to model 1 after the inclusion of interaction variable, i.e. profitability in terms of ROA and ROE. The comparative result shows that the companies of Saudi Arabia hold more debt in their capital structure mix, hence evidencing a decrease in the firm value. The reported results also show that models 2 and 3 are better in explaining the impact of capital structure on firm value due to the interaction of profitability compared to model 1.

• Journal of the Korean Data and Information Science Society
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• v.28 no.3
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• pp.547-557
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• 2017

The health-related quality of life data are commonly skewed and bounded with spike at the perfect health status, and the variance tended to be heteroscedastic. In this study, we have developed a prediction model for EQ-5D using linear regression model, beta regression model, and extended beta regression model with mean and precision submodel, and also compared the predictive accuracy. The extended beta regression model allows to model skewness and differences in dispersion related to covariates. Although the extended beta regression model has higher prediction accuracy than the linear regression model, the overlapped confidence intervals suggested that the extended beta regression model was superior to the linear regression model. However, the expended beta regression model could explain the heteroscedasticity and predict within the bounded range. Therefore, the expended beta regression model are appropriate for fitting the health-related quality of life data such as EQ-5D.