• The Journal of Asian Finance, Economics and Business
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• v.8 no.5
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• pp.303-314
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• 2021

The higher the firm performance, the more chances enterprises can expand and develop their production, create jobs, and improve the workers' living quality. The main objective of this study was to measure the internal factors influencing the firm's performance of food and beverage (F&B) firms listed on the Hanoi Stock Exchange (HNX). Data was collected on 15 F&B firms listed on the HNX from 2015 to 2019 We use mixed research method, both qualitative and quantitative. For the quantitative research method, the supporting tool is Stata13 software. The results via Ordinary Least Squares (OLS) regression method show the impacts of internal factors with the following observed variables: the ratio of short-term debt to total liabilities (CS1) and total assets (S2) have an opposite impact (-) on ROA and ROE; debt-to-total assets ratio (CS2) has an opposite effect (-) on ROA; growth of total assets (G2) of the growth factor positively affects (+) ROA and ROE, the remaining factors do not affect ROA and ROE; and internal factors do not influence ROS. Based on the findings, some recommendations have been proposed to help the F&B firms listed on the Hanoi Stock Exchange improving their firm performance in the future.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.9
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• pp.377-388
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• 2020

The research identifies hypotheses evaluating the impact of board characteristics on the risk of the commercial bank as well as examining the determinants of bank risk in Vietnam over a 10-year period, starting from 2008. Also, in this research, the differences between the roles of women and men in decision-making are tested. Based on this decision, risks of the banks may arise. Ordinary least squares(OLS) regression, Random effect method, and Fixed effect method are used to estimate the factors that have an impact on bank risk for dataset of all commercial banks in Vietnam. The results found that equity-to-asset ratio, bank performance and the economic growth have an inverse relationship with bank risk, while the size of bank has a positive relationship with the bank risk. One of the highlights of this paper is a demonstration of the relationship between CEO's gender and bank risk. The test result shows that the bank led by a female faces a higher overall risk level and credit risk than a bank led by a male. Based on this result, the paper also makes recommendations to Government, the State Bank of Vietnam and the commercial banks for effective risk management.

• 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.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.6
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• pp.9-19
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• 2019

Vegetative Filter Strip (VFS) is the best management practice which has been widely used to mitigate water pollutants from agricultural fields by alleviating runoff and sediment. This study was conducted to improve an equation for estimating sediment trapping efficiency of VFS using several different regularization methods (i.e., ordinary least squares analysis, LASSO, ridge regression analysis and elastic net). The four different regularization methods were employed to develop the sediment trapping efficiency equation of VFS. Each regularization method indicated high accuracy in estimating the sediment trapping efficiency of VFS. Among the four regularization methods, the ridge method showed the most accurate results according to $R^2$, RMSE and MAPE which were 0.94, 7.31% and 14.63%, respectively. The equation developed in this study can be applied in watershed-scale hydrological models in order to estimate the sediment trapping efficiency of VFS in agricultural fields for an effective watershed management in Korea.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.4
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• pp.73-81
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• 2013

Ordinary least squares (OLS) regression is the primary statistical method in previous studies for vegetation distribution patterns in relation to landform. However, this global regression lacks the ability to uncover some local-specific relationships and spatial autocorrelation in model residuals. This study employed geographically weighted regression (GWR) to examine the spatially varying relationships between NDVI (Normalized Difference Vegetation Index) patterns and changing trends of landform (elevation, slope) and solar intensity (insolation and duration of sunshine) in Mt Geum-gang of North-Korea. Results denoted that GWR was more powerful than OLS in interpreting relationships between NDVI patterns and landform/solar intensity, since GWR was characterized by higher adjusted R2, and reduced spatial autocorrelations in model residuals. Unlike OLS regression, GWR allowed the coefficients of explanatory variables to differ by locality by giving relatively more weight to NDVI patterns which are affected by local landform and solar factors. The strength of the regression relationships in the GWR increased significantly, by showing regression coefficient of higher than 70% (0.744) in the southern ridge of the experimental area. It is anticipated that this research output will serve to increase the scientific and objective vegetation monitoring in relation to landform and solar intensity by overcoming serious constraints suffered from the past non-GWR-based approach.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.1
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• pp.43-51
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• 2012

The OLS(ordinary least squares) method is widely used in hedonic housing models. One of the assumptions of the OLS is an independent and uniform distribution of the disturbance term. This assumption can be violated when the spatial autocorrelation exists, which in turn leads to undesirable estimate results. An alterative to this, spatial econometric models have been introduced in housing price studies. This paper describes the comparisons between OLS and spatial econometric models using housing transaction prices of Busan, Korea. Owing to the approaches reflecting spatial autocorrelation, the spatial econometric models showed some superiority to the traditional OLS in terms of log likelihood and sigma square(${\sigma}^2$). Among the spatial models, the SAR(Spatial Autoregressive Models) seemed more appropriate than the SAC(General Spatial Models) and the SEM(Spatial Errors Models) for Busan housing markets. We can make sure the spatial effects on housing prices, and the reconstruction plans have strong impacts on the transaction prices. Selecting a suitable spatial model will play an important role in the housing policy of the government.

• Journal of Korea Water Resources Association
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• v.45 no.8
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• pp.815-826
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• 2012

The underestimating trend using existing ordinary regression (OR) based trend analysis has been a well-known problem. The existing OR method based on least squares approximate the conditional mean of the response variable given certain values of the time t, and the usual assumption of the OR method is normality, that is the distribution of data are not dissimilar form a normal distribution. In this regard, this study proposed a quantile regression that aims at estimating either the conditional median or other quantiles of the response variable. This study assess trend in annual daily maximum rainfall series over 64 weather stations through both in OR and QR approach. The QR method indicates that 47 stations out of 67 weather stations are a strong upward trend at 5% significance level while OR method identifies a significant trend only at 13 stations. This is mainly because the OR method is estimating the condition mean of the response variable. Unlike the OR method, the QR method allows us flexibly to detect the trends since the OR is designed to estimate conditional quantiles of the response variable. The proposed QR method can be effectively applied to estimate hydrologic trend for either non-normal data or skewed data.

• The Journal of Asian Finance, Economics and Business
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• v.6 no.4
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• pp.129-137
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• 2019

Tax can be categorised into direct tax and indirect tax. This paper uses the ordinary least-squares regression method to study the impact of direct and indirect tax on economic growth in Vietnam in the period 2003-2017. Statistical data is collected from the Ministry of Finance of Vietnam. Theoretically, tax generates the state budget revenue and is a tool to regulate the economy. The results of statistical tests show that tax has a positive impact on Vietnam's economic growth. However, the effects of direct tax and indirect tax are different. The indirect tax has a positive influence and promote Vietnam's economic growth, while the impact of the direct tax is invisible. There has not been sufficient evidence to confirm that the indirect tax has a more positive impact than the direct tax. To promote economic growth, Vietnam needs to restructure its tax system towards: (1) Increasing the proportion of indirect tax, reducing the proportion of direct tax in the state budget revenue; (2) Expanding tax bases; (3) Reducing tax rates of corporate income tax and personal income tax; (4) Increasing tax rates of environmental protection tax, natural resources tax, value added tax and excise tax on some types of goods which harm health and environment.

• The Journal of Asian Finance, Economics and Business
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• v.6 no.4
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• pp.99-107
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• 2019

This paper is intended to study the relationship between state budget revenue and economic growth in Vietnam. The ordinary least-squares regression method is used with secondary data collected from General Statistics Office of Vietnam in the period of 2000-2017. Vietnamese state budget revenue includes domestic revenue (excluding oil revenue), oil revenue, custom duty revenue, and grants. The testing result shows that the state budget revenue has a positive correlation with economic growth of Vietnam. However, the components of state budget revenue have different levels of impact on the economy. Domestic revenue and oil revenue are statistically significant and have a positive effect on the economy, while the impact of custom duty revenue and grants on the economy is invisible. Vietnamese state budget revenue should be restructured toward the sustainability and by way of boosting the economy, specifically: (1) Increase the proportion of domestic revenue to state budget revenue and domestic revenue should be based on the ground of production and business activities rather than collection from state-owned assets; (2) Reduce the proportion of custom duty revenue and grants to state budget revenue; (3) Keep the volume and ratio of oil revenue in state budget revenue at an appropriate proportion.

• Asian Journal of Business Environment
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• v.10 no.1
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• pp.29-36
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• 2020

Purpose: This study aims to analyze the effect of macroeconomic and non-macroeconomic determinants of capital flight. Research design, data and methodology: With five determinants, this survey was conducted by Eviews 10, and the ordinary least squares (OLS) as a statistical method was applied for examining the research hypothesis. The five determinants are a budget deficit, economic growth, inflation rate, the exchange rate, and sovereign rating. The capital flight measurement uses the World Bank residual approach. The data derive from the Central Bank of Indonesia, BPS-Statistics Indonesia, OECD, and Moody's Investor Service. Results: The result considers that economic growth, the exchange rate, and the sovereign rating will decrease capital flight. In addition, the budget deficit and the inflation rate will increase capital flight. The sovereign rating decreases capital flight bigger than the other determinants. In addition, the exchange rate is statistically significant. Conclusions: The most influential problem of capital flight in Indonesia is because of non-macroeconomics factor political issue, corruption, bad regulation, and others. That's why the investment climate in Indonesia is still not secure. We propose that the regime would have to amend the business rule for reducing capital, raising the investment climate, and demonstrating the creative industry.