• 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.

• The Korean Journal of Physiology
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• v.10 no.1
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• pp.49-54
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• 1976

The change of heart rates during Flack Test was observed in the pregnant women, $24{\sim}48$weeks, of gestational age, to analyze mechanical and neural regulatory factors in responses to the positive lung inflation. The results obtained were summarized as followings: 1) Endurance tine of Flack Test was 37.6 sec, in the nonpregnant women, and 25.1 sec. in the pregnant women. 2) When Flack Test was employed, heart rate was decreased in early stage of Flack Test in the pregnant women, while heart rate was increased in the nonpregnant women. 3) In the pregnant women bradycardia due to abdominal mechanical intervention in early stage of Flack Test was prominent, while tachycardia was found in the nonpregnant women. 4) During Flack Test, tachycardia due to sympathetic central reflex activation was observed immediately after bradycardia in early stage of Flack Test. 5) It may be noted that Flack Test employed in the present study is a useful model to evaluate and analyze the neural and mechanical abdominal intervention factor in response to the positive inflation of lung in pregnant women.

• Journal of Distribution Science
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• v.21 no.2
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• pp.1-6
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• 2023

Purpose: Research on attracting foreign direct investment plays an important role in ASEAN countries. ASEAN has needed FDI capital for development and integration with many developing countries. Research design, data and methodology: This study is conducted to assess the impact of factors: inflation (INF), economic growth (GDP), population (POP), and trade (TRADE) on attracting foreign direct investment (FDI) of ASEAN countries. The study will find out how factors distribution contributes to FDI attraction. The study collects data from 10 ASEAN countries from 2010 to 2020. With data collected for ten countries from 2010 to 2020, data analysis with panel data will be used in this study. The Regression with Driscoll-Kraay standard errors correction model will be used in the study. Results: Panel data analysis shows that economic growth and population positively impact FDI attraction in ASEAN countries. However, two factors: INF and TRADE, do not affect FDI. Conclusions: Countries need to focus on economic development, create many good conditions for people and domestic enterprises and create opportunities for foreign investors to pay more attention. improving the quality of domestic human resources will help to better improve the working quality factor when the demand for high-quality human resources increases.

• The Journal of the Convergence on Culture Technology
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• v.8 no.4
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• pp.347-353
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• 2022

In the restaurant industry, start-ups are active due to high demand from consumers and low entry barriers. However, the restaurant industry has a high closure rate, and in the case of franchises, there is a large deviation in sales within the same brand. Thus, research is needed to prevent the closure of food franchises. Therefore, this study examines the factors affecting franchise sales and uses machine learning techniques to predict the success and failure of franchises. Various factors that affect franchise sales are extracted by using Point of Sale (PoS) data of food franchise and public data in Gangnam-gu, Seoul. And for more valid variable selection, multicollinearity is removed by using Variance Inflation Factor (VIF). Finally, classification models are used to predict the success and failure of food franchise stores. Through this method, we propose success and failure prediction model for food franchise stores with the accuracy of 0.92.

• The Journal of Engineering Geology
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• v.31 no.4
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• pp.701-718
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• 2021

Geological field surveys and a series of laboratory tests were conducted to obtain data related to landslides in Sancheok-myeon, Chungju-si, Chungcheongbuk-do, South Korea where many landslides occurred in the summer of 2020. The magnitudes of various factors' influence on landslide occurrence were evaluated using logistic regression analysis and an artificial neural network. Undisturbed specimens were sampled according to landslide occurrence, and dynamic cone penetration testing measured the depth of the soil layer during geological field surveys. Laboratory tests were performed following the standards of ASTM International. To solve the problem of multicollinearity, the variation inflation factor was calculated for all factors related to landslides, and then nine factors (shear strength, lithology, saturated water content, specific gravity, hydraulic conductivity, USCS, slope angle, and elevation) were determined as influential factors for consideration by machine learning techniques. Minimum-maximum normalization compared factors directly with each other. Logistic regression analysis identified soil depth, slope angle, saturated water content, and shear strength as having the greatest influence (in that order) on the occurrence of landslides. Artificial neural network analysis ranked factors by greatest influence in the order of slope angle, soil depth, saturated water content, and shear strength. Arithmetically averaging the effectiveness of both analyses found slope angle, soil depth, saturated water content, and shear strength as the top four factors. The sum of their effectiveness was ~70%.

• Communications for Statistical Applications and Methods
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• v.17 no.5
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• pp.697-712
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• 2010

This paper is concerned with the statistical analysis and development of stochastic models for the demand for life insurance policy loans. For these, firstly the characteristics of the regression trend, periodicity and dependence of the monthly demand for life insurance policy loans are investigated by a statistical analysis of the monthly demand data for the years 1999 through 2008. Secondly, the causal relationships between the demand for life insurance policy loans and the economic variables including unemployment rate and inflation rate for the period are investigated. The results show that inflation rate is main factor influencing policy loan demands. The overall evidence, however, failed to establish unidirectional causality relationships between the demand series and the other variables under study. Finally, based on these, univariate time series model and transfer function model where the demand series is related to one input series are derived, respectively, for the prediction of the demand for life insurance policy loans. A statistical procedure for using the model to predict the demand for life insurance policy loans is also proposed.

• Journal of Navigation and Port Research
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• v.41 no.4
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• pp.181-188
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• 2017

In the existing ship sales market, prices determined based on the prices of similar ship types that recently traded. ince the 2008 financial crisis, ship prices have fluctuated, and ship price criteria have become ever more necessary to the imminent value of the ship. Therefore, this research used the hedonic price model to estimate imminent values of ships. In this study, the influence on ship prices was analyzed by the value of each characteristic and an estimated functional formula was. Out of the four models suggested by the hedonic price model, an optimal model was selected with variance inflation factors and a stepwise selection. For this, the influence of determinants of ship prices was analyzed based on actually traded ships and characteristic data. The selected model s the Log-Line model; as a result of regression analysis, eight variables, including DWT, Age, Market Value, Short-Term Charter, Long-Term Charter, Enbloc, Special Survey Due and Builder were to affect the ship price model. This model is expected to be useful for objective and balanced ship price evaluation.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.373-376
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• 2000

We investigate some properties of commonly used residual plots in linear regression and provide some systematic insight into the relationships among the plots. We discuss three issues of linear regression in this stream of context. First of all, we introduce two graphical comparison methods to display the variance inflation factor. Secondly, we show that the role of a suppressor variable in linear regression can be checked graphically. Finally, we show that several other types of standardized regression coefficients, besides the ordinary one, can be obtained in residual plots and the correlation coefficients of one of these residual plots can be used in ranking the relative importance of variables.

• Korean Management Science Review
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• v.19 no.1
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• pp.179-188
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• 2002

Ventures through technological innovation are increasingly suggested as one of the main engines for economic growth that can help control inflation and black trade balance. The Purpose of this study is to extract the network performance factors for ventures according to ventures life cycle. For the Purpose, the existing studies were examined into start-up company, entrepreneurial firm, smell firms with competitive advantage against large firms, and ventures network activity. And 63 samples from ventures in Korea were taken and analyzed empirically. The analyses and results are (1) the actual conditions of network activity on ventures; (2) the investigation of relationship between network activity and performance of ventures by venture's life cycle through the observations of Korean ventures. From the results, It Is also found that factors such as external environment and a ventures life cycle have been considered as the main influences on the performance of ventures. In addition, limitations and suggestions for further studies are noted.

• Communications for Statistical Applications and Methods
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• v.7 no.1
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• pp.37-46
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• 2000

We investigate some properties of commonly used residual plots in linear regression and provide some systematic insight into the relationships among the plots. We discuss three issues of linear regression in this stream of context. First of all we introduce two graphical comparison methods to display the variance inflation factor. Secondly we show that the role of a suppressor variable in linear regression can be checked graphiclly. Finally we show that several other types of standardized regression coefficients besides the ordinary one can be obtained in residual plots and the correlation coefficients of one of these residual plots can be used in ranking the relative importance of variables.