• Journal of Distribution Science
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• v.14 no.6
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• pp.39-44
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• 2016

Purpose - This paper aims to study the competition that occurs in the Islamic Banking industry and to analyze the variables that affect the total revenue of Islamic Banking in Indonesia. Research Design, Data and Methodology - This study observed 10Islamic banks for the period 2010-2013. The annual data are taken from Direktori Perbankan Indonesia, published by Bank Indonesia, and annual report of the observed banks. In analyzing data, Panzar Rosse Approach was applied to analyze the type of Islamic Bank Market and Panel Regression Model for the estimated co-efficients has been used in the Panzar Rosse Approach. Results - Estimation model shows that all the banking cost elements such as the price of capital, unit price of labor, and unit prices of funds have significant positive correlation to Revenue as a dependent variable. The estimated value of H-statistic for the period 2010-2013 is 0.69. It can be interpreted that Islamic banking market in Indonesia shows monopolistic competition. Price of capital and funds has statistically significant effect on Bank's Revenue. Conclusions - The study revealed that the Islamic banking market competition in Indonesia is monopolistic and the major contribution to the H-statistic comes from mainly price of funds.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.11
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• pp.689-698
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• 2020

The aim of this article is to determine the influence of factors on the capital structure of construction companies listed on the Hanoi Stock Exchange. The data of the article were collected and calculated from the financial statements of 54 construction companies listed on Hanoi Stock Exchange from 2012 to 2019. With the application of E-view software in quantitative analysis to build panel data regression model (panel data), the article has built a regression model to determine the relationship of intrinsic factors affecting the capital structure of construction companies listed on Hanoi Stock Exchange. In the study, dependent variable is capital structure, determined by the debt-to-equity ratio. Profitability, coefficient of solvency, size, loan interest rate, structure of tangible assets, and growth are independent variables. The results showed that the two factors of growth and firm size positively affect the capital structure, the profitability factor has the opposite effect on capital structure. Factors of short-term debt solvency, average loan interest rate and tangible asset structure have no correlation with capital structure. The findings of this article are useful for business administrators, helping business managers make the right financial decisions to make capital structure decisions in their own conditions.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.12
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• pp.95-105
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• 2021

A prerequisite of a sound financial system is effective channeling of financial resources to efficient users; hence maximizing economic and societal welfare. To that end, the prevalence of bad loans in banks in emerging economies is a major policy concern. In an attempt to add to the growing body of literature explaining the interrelationship between macroeconomic and firm-specific factors, and non-performing loans (NPL), this paper examines data from 24 scheduled commercial banks in Bangladesh from 2008 to 2019. Macroeconomic factors as well as firm-specific factors related to profitability, capital strength, and efficiency are considered. Panel data regression analysis is performed to estimate pooled OLS, fixed effects, and random effects models. Following the necessary testing, it was found that the fixed effects model with robust standard error is appropriate. Results show that return on assets and inflation have a negative influence on NPL, but GDP growth has a favorable impact. The paper concludes by asserting that the evidence supports similar findings from studies both in Bangladesh and elsewhere and it is noted that a combination of these macroeconomic and firm-specific factors explains only a small portion of the total variation in NPL.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1147-1154
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• 2016

Impact of Korea train express (KTX) on the regional economy in Korea has been studied by many researchers. Current research is limited in the lack of quantitative research using a statistical model to study the effect of KTX on regional economy. This paper analyses the influence of KTX to the household final consumption expenditure, which is one of important regional economic index, using spatial panel regression model. The spatial structure is introduced through spatial autocorrelation matrix using adjacency of KTX connection. The result shows a significant effect of Korea train express on the regional economy.

• Journal of Digital Convergence
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• v.15 no.4
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• pp.327-337
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• 2017

The purpose of this study is to identify the current state of paid care services and to identify the factors that affect the utilization of private nursing services. This study constructed and utilized the Korean Health Panel data (2011-2014) in the form of panel data, and selected 5,110 patients who had experience using one or more hospitalization services per year. STATA 12.0 SE was used for data processing and analysis of this study. Frequency analysis was performed to confirm basic characteristics of hospitalized patients. Cross-analysis and t-test were conducted to confirm the status of paid care services according to characteristics. Respectively. Finally, panel logistic regression was performed by applying a hierarchical method to stepwise modeling the three categories of Andersen's Behavioral Model to identify factors affecting the use of paid care services for inpatients. The results showed that the use of paid nursing services was higher in women, elderly, long - term hospitalized and disabled. On the other hand, significant household income variables in private employment did not show significant results. The results of this study are expected to be used as basic data for the selection of the nursing care integrated services under discussion. In addition, detailed discussions on the selection of subjects should be made in the future.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.12
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• pp.491-497
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• 2018

This study analyzes the relationship between R&D input and performance using panel data from the defense industry. A research model is established based on the R&D logic model, and the study sample consists of a strongly balanced panel data (n=351) empirically analyzed using panel linear regression. Results identified that defense improvement expenditure has a positive influence on the R&D input, and R&D input positively affected patents using a 5-year time lag. In addition, R&D input positively impacts economic performance, including sales and profit. Hence, the major finding includes R&D inputs have statistically significant effects on economic outcome and the R&D logic model featuring a time-lag.

• International Commerce and Information Review
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• v.14 no.3
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• pp.307-322
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• 2012

The purpose of this paper is to examine determinants of export to the East Asia region, using panel unit root, panel cointegration framework, panel VECM (vector error correction model), panel FMOLS (fully modified OLS). Different panel unit root tests confirm that the data series are integrated processes with unit roots. When applying cointegration tests to long-run effect for aggregate panel data, a primary concern is to construct the estimators in a way that does not constrain the transitional dynamics to be similar among different countries of the panel. The regression equations are estimated by various panel cointegration estimators. The panel data causality results reveal that exchange rates has unidirectional effects on export and GDP, and there exists bidirectional causality between export and GDP. Also, the results from the panel FMOLS tests overwhelmingly reject the null hypothesis of zero coefficient. The panel cointegrating vectors show that the export has positive relationship with the GDP and ODI (overseas direct investment).

• Communications for Statistical Applications and Methods
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• v.2 no.2
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• pp.129-134
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• 1995

The ordinary least square estimator of the disturbance variance in the pooled cross-sectional and time series regression model is shown to be asymptotically unbiased without any restrictions on the regressor matrix when the disturbances follow an equicorrelated error component models.

• Environmental and Resource Economics Review
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• v.33 no.1
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• pp.59-86
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• 2024

Recently, research on city gas demand is increasing by reflecting the characteristics of each region. The similarity of the social structure of the adjacent region and the density of the supply infrastructure induce spatial correlation with the clustering that has a microscopic relationship between regions. Accordingly, as a result of analyzing the spatial correlation after dividing the demand for city gas for civilian use into a total of 54 regions based on the jurisdiction of 34 city gas companies, it was confirmed that there was a positive spatial correlation from a global and local perspective. In this study, the demand for city gas for civilian use for 54 regions from January 2014 to December 2022 was composed of panel data, and the spatial panel regression analysis and the general panel regression analysis were compared, and it was found that the spatial error model (SEM) was the most suitable model. This presents policy and practical implications by confirming that the demand for city gas for civilian use in one region has a significant relationship with the adjacent region.

• Communications for Statistical Applications and Methods
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• v.19 no.2
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• pp.293-301
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• 2012

Quantile regression proposed by Koenker and Bassett (1978) is a statistical technique that estimates conditional quantiles. The advantage of using quantile regression is the robustness in response to large outliers compared to ordinary least squares(OLS) regression. A regression tree approach has been applied to OLS problems to fit flexible models. Loh (2002) proposed the GUIDE algorithm that has a negligible selection bias and relatively low computational cost. Quantile regression can be regarded as an analogue of OLS, therefore it can also be applied to GUIDE regression tree method. Chaudhuri and Loh (2002) proposed a nonparametric quantile regression method that blends key features of piecewise polynomial quantile regression and tree-structured regression based on adaptive recursive partitioning. Lee and Lee (2006) investigated wage determinants in the Korean labor market using the Korean Labor and Income Panel Study(KLIPS). Following Lee and Lee, we fit three kinds of quantile regression tree models to KLIPS data with respect to the quantiles, 0.05, 0.2, 0.5, 0.8, and 0.95. Among the three models, multiple linear piecewise quantile regression model forms the shortest tree structure, while the piecewise constant quantile regression model has a deeper tree structure with more terminal nodes in general. Age, gender, marriage status, and education seem to be the determinants of the wage level throughout the quantiles; in addition, education experience appears as the important determinant of the wage level in the highly paid group.