• Journal of Preventive Medicine and Public Health
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• v.56 no.1
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• pp.59-66
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• 2023

Objectives: The aim of this study was to evaluate the effect of body weight status and sleep duration on the discrete-time hazard of menarche in Korean schoolgirls using multiple-point prospective panel data. Methods: The study included 914 girls in the 2010 Korean Children and Youth Panel Study who were in the elementary first-grader panel from 2010 until 2016. We used a Gompertz regression model to estimate the effects of weight status based on age-specific and sex-specific body mass index (BMI) percentile and sleep duration on an early schoolchild's conditional probability of menarche during a given time interval using general health condition and annual household income as covariates. Results: Gompertz regression of time to menarche data collected from the Korean Children and Youth Panel Study 2010 suggested that being overweight or sleeping less than the recommended duration was related to an increased hazard of menarche compared to being average weight and sleeping 9 hours to 11 hours, by 1.63 times and 1.38 times, respectively, while other covariates were fixed. In contrast, being underweight was associated with a 66% lower discrete-time hazard of menarche. Conclusions: Weight status based on BMI percentiles and sleep duration in the early school years affect the hazard of menarche.

• The Korean Journal of Applied Statistics
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• v.29 no.5
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• pp.873-885
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• 2016

Onions are grown in a few specific regions of Korea that depend on the climate and the regional characteristic of the production area. Therefore, when onion yields are to be estimated, it is reasonable to use a statistical model in which both the climate and the region are considered simultaneously. In this paper, using a spatial panel regression model, we predicted onion yields with the different weather conditions of the regions. We used the spatial auto regressive (SAR) model that reflects the spatial lag, and panel data of several climate variables for 13 main onion production areas from 2006 to 2015. The spatial weight matrix was considered for the model by the threshold value method and the nearest neighbor method, respectively. Autocorrelation was detected to be significant for the best fitted model using the nearest neighbor method. The random effects model was chosen by the Hausman test, and the significant climate variables of the model were the cumulative duration time of sunshine (January), the average relative humidity (April), the average minimum temperature (June), and the cumulative precipitation (November).

• The Korean Journal of Applied Statistics
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• v.19 no.3
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• pp.521-534
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• 2006

This paper considers a panel regression model with ill-posed data and proposes the generalized maximum entropy(GME) estimator of the unknown parameters. These are natural extensions from the biometries, statistics and econometrics literature. The performance of this estimator is investigated by using of Monte Carlo experiments. The results indicate that the GME method performs the best in estimating the unknown parameters.

• Journal of Environmental Science International
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• v.22 no.9
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• pp.1171-1180
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• 2013

Recent climate change has led to fluctuations in agricultural production, and as a result national food supply has become an important strategic factor in economic policy. As such, in this study, panel data was collected to analyze the effects of seven meteorological elements and using the Lagrange multipliers method, the fixed-effects model for the production of five types of food crop and the seven meteorological elements were analyzed. Results showed that the key factors effecting increases in production of rice grains were average temperature, average relative humidity and average ground surface temperature, while wheat and barley were found to have positive correlations with average temperature and average humidity. The implications of this study are as follow. First, it was confirmed that the meteorological elements have profound effects on the production of food crops. Second, when compared to existing studies, the study was not limited to one food crop but encompassed all five types, and went beyond other studies that were limited to temperature and rainfall to include various meterological elements.

• Journal of Environmental Science International
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• v.27 no.3
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• pp.147-154
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• 2018

The purpose of this article is analyzing the impacts of seawater surface temperature rise on sea mustard yields of Goheung and Wando coast in Korea, with employing a panel data regression model. Our results show that there has been a negative impacts on sea mustard yields as seawater surface temperature continuously has been rising. Especially if the upward trend in seawater surface temperature since 2005 will be maintained in future, sea mustard yield is expected to decrease by 2.6% per year.

• 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.

• Health Policy and Management
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• v.14 no.2
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• pp.99-116
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• 2004

This study estimates the determinants of national health expenditures of OECD countries using panel regression method. The data used are OECD Health Data(2003) covering 33 countries and from 1970 to 2001. This study shows several important different results compared to the previous studies. Further this study estimates the determinants of Korean case using data from 1m to 2000, and compare with the results of OECD panel. The main findings are as follows. The income elasticity of health expenditures is estimated below 1.0, but is shown above 1.0 when the different health systems of each country are controlled. The women's labor participation influences strongly positive effect on the health expenditures. The diffusion of new technologies is positively related with the increasing expense. The increasing government expenditures have a tendency not to contain health expenses, but to increase expenses. The expansion of public health insurance holders is containing the expenses, and the increasing number of doctors is pushing expenditures. This implies the health expenditures are influenced more by the induced demand of providers rather than the moral hazard of patients. However, the above result is opposite in Korean case. The existence of primary care doctors affects slightly up warding rather than containing expenditures. Finally the determinants are seriously depending upon which factors are included in the model and which statistical model is chosen. Therefore it must be cautious to interpret the results of statistical model.

• East Asian Economic Review
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• v.26 no.2
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• pp.143-169
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• 2022

This study investigates the effect of economic factors on immigration using the gravity model of immigration. Cross-sectional regression and panel data analyses are conducted from 2000 to 2019 using the OECD International Migration Database, which consists of 36 destination countries and 201 countries of origin. The Poisson pseudo-maximum-likelihood method, which can effectively correct potential biased estimates caused by zeros in the immigration data, is used for estimation. The results indicate that the economic factors strengthened after the global financial crisis. Additionally, this effect varies depending on the type of immigration (the income level of origin country). The gravity model applied to immigration performs reasonably well, but it is necessary to consider the country-specific and time-varying characteristics.

• Asian Journal of Business Environment
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• v.13 no.1
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• pp.31-41
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• 2023

Purpose: This study purposes to analyze the determinants of the volume of Indonesian tuna exports. Research design, data and methodology: The framework was developed from the gravity model for trade, which was expanded with additional variables of competitiveness, exchange rate, and industrial share of the destination country. The data sources used in this study are UN Comtrade and the World Bank. The data used is yearly data from 12 countries in 2001-2019. The scope of the study is limited to exports to the twelve main export destinations. Panel data regression analysis is used to determine the factors that affect the volume of Indonesian tuna exports. Results: The results show that according to the theory, Indonesia's GDP has a positive effect and economic distance has a negative effect on the volume of the exports. Meanwhile, the GDPs of the destination countries are not proven to have a positive effect. However, the higher the industrial share in the country, the higher the export volume tends to be. Conclusions: The conclusion obtained from this study is that Indonesia's GDP, economic distance, real exchange rate, industrial GDP share of the destination country, and the RCA index affect the volume of Indonesian tuna exports.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.7
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• pp.43-54
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• 2021

Human capital and innovation capacities are essential elements and one of the sustainable approaches to driving economic growth. However, there is debate among scholars concerning these two factors in fostering economic growth. This study investigates the relationships between human capital and innovation capacity and economic growth in selected ASEAN countries, namely, Malaysia, Thailand, and Indonesia. Economists widely discussed the interrelation of human capital and innovation. A large body of literature stated that human capital is an essential factor and engine of economic growth. Innovation has become key in transforming the economic development of developing countries. We analyze human capital (HC) and innovation capacity (INC) using static panel data analysis. The data analysis shows that the fixed-effect model is the best model in this study. Further, human capital (HC) has a significant positive relationship with economic growth. Meanwhile, innovation capacity has no significant relationship with economic growth. We also found that Malaysia's coefficient of human capital and innovation capacity is higher and more efficient than in Thailand and Indonesia. In conclusion, human capital and innovation capacity are crucial elements for measuring economic growth. Skilled human capital contributes significantly to the economic growth and economic development of a nation.