• Health Policy and Management
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• v.25 no.3
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• pp.152-161
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• 2015

We analyze the determinants of obesity and the chronic diseases using the Korea Health Panel data. Also we analyze the effect of obesity and the chronic diseases on the health care expenditures. Through this study, to reduce the health care expenditures, we suggest the policy implication that might curb the obesity and the chronic diseases. We estimate the determinants of obesity, the chronic diseases, and the health care expenditures using 2SLS (two stage least squares) estimation method under the simultaneous equations framework. Result says that obesity and chronic diseases significantly have positive effects on the health care expenditures. Also the determinants of the health care expenditures that have positive effects are age, income and health care utilization variables.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.11
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• pp.41-46
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• 2021

The purpose of this study is to investigate the relationship between unemployment and shadow economy for 7 selected ASEAN countries using panel data from 2000-2017. This study uses a sample of 7 ASEAN countries including Cambodia, Indonesia, Malaysia, the Philippines, Singapore, Thailand, and Vietnam covering the 2000-2017 period. The stationarity of the variables is determined by Pesaran panel unit-root tests. The Westerlund panel co-integration technique is used to examine the long-run relationship among the variables. In addition, dynamic ordinary least squares (DOLS) and fully modified ordinary least squares (FMOLS) methods are also employed. The DOLS and FMOLS results indicate that unemployment acts as an important driver for the increase in the shadow economy. In addition, the study results also reveal that GDP per capita has a negative impact on the shadow economy. Moreover, government expenditure, bank credit, and inflation are positively related to the shadow economy. The empirical results indicate that the size of the shadow economy is boosted by unemployment in the selected ASEAN economies. In addition, it is also evident that an increase of GDP per capita in the sample countries results in a lower shadow economy. Besides, government expenditure, bank credit, and inflation play a crucial role in the shadow economy.

• 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.

• Proceedings of the KOR-KST Conference
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• 1998.09a
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• pp.160-167
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• 1998

Recent world-wide interest in activity-based travel behavior modeling has generated an entirely new perspective on how the profession views the travel demand process. This paper seeks to further promote the case of activity-based travel behavior models by providing some empirical evidence of relationship between travel time and activity duration decision for worker with transportation panel data. The travel time from home to work and from work to home, without activity involvement, is estimated by the Ordinary Least Squares (OLS) method. And, the travel time to and from the selected activity and the activity duration are modeled simultaneously by the Three Stage Least Squares (3SLS) method due to the endogenous relationship between travel time and activity duration. Two kinds of models, OLS and 3SLS, include selectivity bias corrections in a discrete/continuous framework, because of the inter-relationship between the choice of activity type/travel mode (discrete) and the travel time/activity duration (continuous). Estimation is undertaken using a sample of over 1300 household two-day trip diaries collected from the same travelers in the Seattle area in 1989. The behavioral consequences of these models provide interesting and provocative findings that should be of value to transportation policy formulation and analysis.

• The Journal of Asian Finance, Economics and Business
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• v.6 no.2
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• pp.95-104
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• 2019

Along with the economic globalization and network generalization, this provides a good opportunity to the development of cross-border e-commerce trade. Based on this background, this paper sets ASEAN countries as an example to exploit the determinants of cross-border e-commerce trade including the export and the import, respectively. The panel data from the year of 1998 to 2016 will be employed to estimate the relationship between cross-border e-commerce trade and relevant variables under the dynamic ordinary least squares and the error correction model. The findings of this paper show that there is a long-run relationship between cross-border e-commerce trade and relevant variables. Generally speaking, the GDP(+) and real exchange rate(-export & +import) have an effect on cross-border e-commerce trade. However, the population (+) and the terms of trade (-) only have an effect on cross-border e-commerce import. The empirical evidences show that the GDP and the real exchange rate always affect the development of cross-border e-commerce trade. Therefore, all ASEAN countries should try their best to develop the economic growth and focus on the exchange rate regime so as to meet the need of cross-border e-commerce trade development.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.3
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• pp.229-234
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• 2022

The study aims to investigate whether Musharakah management has an impact on Chinese and Malaysian business partnerships. To estimate the relationship between Musharakah and the Sino-Malaysian partnership, this study uses a panel econometric technique namely pooled ordinary least squares. Ordinary Least Squares regression (OLS) is a common technique for estimating coefficients of linear regression equations which describe the relationship between one or more independent quantitative variables and a dependent variable. Data was retrieved from the annual reports (from 2009 to 2019) of non-financial firms listed on the stock exchange of China and Malaysia. Four partnership measures (i.e., Musharakah, Mudarabah, Tawuruq, and Kafalah) were used to estimate the impact of Musharakah on the Sino-Malaysian partnership. Empirical results reveal that Musharakah and Mudarabah are positively related to Kafalah but the relationship is statistically insignificant. Alternatively, Musharakah is positively and significantly related to Mudarabah. Musharakah and Mudarabah have a positive but insignificant relationship. The findings of this study suggest that management of partnership has a positive impact on firm partnership. Furthermore, it supports the hypothesis that improving partnership enhances Musharakah, which has a positive impact on the firm's partnership.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.4
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• pp.121-131
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• 2020

This study aims to investigate the interplay between education and local roads on Foreign Direct Investment (FDI) in the Philippines, using economic growth as an instrument. The study used the quantitative research design applying both descriptive and inferential statistics. A combination of Two Stage Least Square Regression Model and three approaches in Panel Regression Model such as Pooled Least Square, Fixed Effect Model, and Random Effect Model were utilized in order to study the effects of education and local roads on foreign direct investment of the Philippines. Based on Fixed Effect regression results, higher education graduates and local road investments, as conditioned by economic growth, were significant factors in order to increase the foreign direct investment in the Philippines. Accordingly, a unit increase in higher education graduates, as conditioned by economic growth, leads to 8.758 unit increases in the foreign direct investment. While, a unit increased in local road investments, as conditioned by economic growth, leads to a 0.002 decrease in foreign direct investment. The regression results of the study suggest that the Foreign Direct Investment in the regions such as CAR, I, II, IV-B, V, VIII, IX, X, XI, XII, XIII, and ARMM are higher compared to Region IV-A.

• Communications for Statistical Applications and Methods
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• v.21 no.5
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• pp.395-409
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• 2014

Data limited, partial, or incomplete are known as an ill-posed problem. If the data with ill-posed problems are analyzed by traditional statistical methods, the results obviously are not reliable and lead to erroneous interpretations. To overcome these problems, we propose a dual generalized maximum entropy (dual GME) estimator for panel data regression models based on an unconstrained dual Lagrange multiplier method. Monte Carlo simulations for panel data regression models with exogeneity, endogeneity, or/and collinearity show that the dual GME estimator outperforms several other estimators such as using least squares and instruments even in small samples. We believe that our dual GME procedure developed for the panel data regression framework will be useful to analyze ill-posed and endogenous data sets.

• Journal of Korea Port Economic Association
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• v.40 no.1
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• pp.119-131
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• 2024

This study examines the interrelationship between coastal passenger demand and fares for 101 coastal passenger routes in Korea during the 2018 to 2022 period. The two-stage least squares method through a panel data simultaneous equations model was estimated to the effects of individual route characteristics and regional characteristics on the performance and fares of coastal passenger transportation. The estimated results indicate that the endogenous variable, fare, and the exogenous variables, route characteristics, route distance, and the instrumental variable, frequency, affect the demand for coastal passengers. In the short-run pricing function, the exogenous variables, capacity, speed, and route distance, as well as the endogenous variable, coastal passenger transportation performance, affect the coastal passenger fare. This study is expected to provide useful implications for domestic coastal passenger demand and pricing in relation to coastal passengers.

• Proceedings of the Korean Statistical Society Conference
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• 2002.11a
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• pp.265-272
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• 2002

An Integer-valued autoregressive integrated (INARI) model is introduced to eliminate stochastic trend and seasonality from time series of count data. This INARI extends the previous integer-valued ARMA model. We show that it is stationary and ergodic to establish asymptotic normality for conditional least squares estimator. Optimal estimating equations are used to reflect categorical and serial correlations arising from panel count data and variations arising from three random processes for obtaining observation into estimation. Under regularity conditions for martingale sequence, we show asymptotic normality for estimators from the estimating equations. Using cancer mortality data provided by the U.S. National Center for Health Statistics (NCHS), we apply our results to estimate the probability of cells classified by 4 causes of death and 6 age groups and to forecast death count of each cell. We also investigate impact of three random processes on estimation.