• The Journal of Asian Finance, Economics and Business
• /
• v.9 no.2
• /
• pp.37-47
• /
• 2022

This study uses monthly data from January 2009 to December 2020 to examine the effectiveness of foreign currency intervention and its influence on monetary policy in Vietnam using a Hierarchical Bayesian VAR model. The findings suggest that foreign exchange intervention has little influence on the exchange rate level or exports, but it can significantly minimize exchange rate volatility. As a result, we can demonstrate that the claim that Vietnam is a currency manipulator is false. As well, the forecast error variance decomposition results reveal that interest rate differentials mainly determine the exchange rate level instead of foreign exchange intervention. Moreover, the findings suggest that foreign exchange intervention is not effectively sterilized in Vietnam. Inflation is caused by an increase in international reserves, which leads to an expansion of the money supply and a decrease in interest rates. Although the impact of foreign exchange intervention grows in tandem with the growth of international reserves, if the sterilizing capacity does not improve, rising foreign exchange intervention will instead result in inflation. Finally, we use a rolling window approach to examine the time-varying effect of foreign exchange intervention.

• Journal of the Korean Statistical Society
• /
• v.28 no.3
• /
• pp.311-324
• /
• 1999

The problem of 'outliers', observations which look suspicious in some way, has long been one of the most concern in the statistical structure to experimenters and data analysts. We propose a model for an outlier problem and also analyze it in linear regression model using a Bayesian approach. Then we use the mean-shift model and SSVS(George and McCulloch, 1993)'s idea which is based on the data augmentation method. The advantage of proposed method is to find a subset of data which is most suspicious in the given model by the posterior probability. The MCMC method(Gibbs sampler) can be used to overcome the complicated Bayesian computation. Finally, a proposed method is applied to a simulated data and a real data.

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

• Korean Journal of Construction Engineering and Management
• /
• v.16 no.5
• /
• pp.77-85
• /
• 2015

The Hedonic Pricing model is the predominant approach used today to model the effect of relevant factors on real estate prices. These factors include intrinsic elements of a property such as floor areas, number of rooms, and parking spaces. Also, The model also accounts for the impact of amenities or undesirable facilities of a property's value. In the latter case, euclidean distances are typically used as the parameter to represent the proximity and its impact on prices. However, in situations where multiple facilities exist, multi-colinearity may exist between these parameters, which can result in multi-regression models with erroneous coefficients. This research uses Variance Inflation Factors(VIF) and Ridge Regression to identify these errors and thus create more accurate and stable models. The techniques were applied to apartments in Guro-gu of Seoul, whose prices are impacted by subway stations as well as a public prison, a railway terminal and a digital complex. The VIF identified colinearity between variables representing the terminal and the digital complex as well as the latitudinal coordinates. The ridge regression showed the need to remove two of these variables. The case study demonstrated that the application of these techniques were critical in developing accurate and robust Hedonic Pricing models.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.60 no.1
• /
• pp.111-120
• /
• 2018

The purpose of this study was to develop a ridge regression (RR) model to estimate BOD and TP load using runoff weighted value. The concept of runoff weighted value, based on distributed curve-number (CN), was introduced to reflect the impact of land covers on runoff. The estimated runoff depths by distributed CN were closer to the observed values than those by area weighted mean CN. The RR is a technique used when the data suffers from multicollinearity. The RR model was developed for five flow duration intervals with the independent variables of daily runoff discharge of seven land covers and dependent variables of daily pollutant load. The RR model was applied to Heuk river watershed, a subwatershed of the Han river watershed. The variance inflation factors of the RR model decreased to the value less than 10. The RR model showed a good performance with Nash-Sutcliffe efficiency (NSE) of 0.73 and 0.87, and Pearson correlation coefficient of 0.88 and 0.93 for BOD and TP, respectively. The results suggest that the methods used in the study can be applied to estimate pollutant load of different land cover watersheds using limited data.

• International Area Studies Review
• /
• v.13 no.3
• /
• pp.309-330
• /
• 2009

This paper analyzes comparatively the effects of changes in real effective exchange rates on the business fluctuations of the cases of Korea and Japan employing structural vector auto-regression(S-VAR) model which uses quarterly data for the five variables of real effective exchange rates, GDP gap, real interest rates, oil prices, inflation rates for the period of 1980-2006. The paper employes impulse-response analysis and variance decompositions. The paper finds that real exchange rate depreciations are contractionay for the case of Korea while they are expansionary for the case of Japan. These results are consistent with the prevailing empirical results that real exchange rate depreciations are contractionary for developing countries while expansionary for advanced countries.

• Journal of Preventive Medicine and Public Health
• /
• v.57 no.5
• /
• pp.435-442
• /
• 2024

Objectives: The aim of this study was to investigate the significance of associations between knowledge, professional ethics, institutional support, perceptions regarding HIV/AIDS, and HIV/AIDS-related stigma among health workers in West Sumatra, Indonesia. Methods: We conducted a cross-sectional study involving health workers at public hospitals and health centers in West Sumatra in June 2022. The Health Care Provider HIV/AIDS Stigma Scale was employed to assess the stigma associated with HIV/AIDS. To estimate and evaluate the model's ability to explain the proposed constructs, we utilized the standardized partial least squares structural equation model (PLS-SEM). Results: In total, 283 individuals participated in this study (average age, 39 years). The majority were female (91.2%), nearly half were nurses (49.5%), and 59.4% had been working for more than 10 years. The study revealed that HIV/AIDS-related stigma persisted among health workers. The PLS-SEM results indicated that all latent variables had variance inflation factors below 5, confirming that they could be retained in the model. Knowledge and professional ethics significantly contributed to human immunodeficiency virus (HIV)-related stigma, with an effect size (f²) of 0.15 or greater. In contrast, perceived and institutional support had a smaller impact on HIV-related stigma, with an effect size (f²) of at least 0.02. The R² value for health worker stigma was 0.408, suggesting that knowledge, professional ethics, institutional support, and perceived support collectively explain 40.8% of the variance in stigma. Conclusions: Improving health workers' understanding of HIV, fostering professional ethics, and strengthening institutional support are essential for reducing HIV-related stigma in this population.

• The Korean Journal of Applied Statistics
• /
• v.31 no.4
• /
• pp.463-473
• /
• 2018

Variable selection algorithms for linear regression models of large data are considered. Many algorithms are proposed focusing on the speed and the robustness of algorithms. Among them variance inflation factor (VIF) regression is fast and accurate due to the use of a streamwise regression approach. But a VIF regression is susceptible to outliers because it estimates a model by a least-square method. A robust criterion using a weighted estimator has been proposed for the robustness of algorithm; in addition, a robust VIF regression has also been proposed for the same purpose. In this article a fast and robust variable selection method is suggested via a VIF regression with detecting and removing potential outliers. A simulation study and an analysis of a dataset are conducted to compare the suggested method with other methods.

• Journal of Biosystems Engineering
• /
• v.39 no.3
• /
• pp.151-157
• /
• 2014

Purpose: In order to develop strategies to prevent farm-work accidents relating to agricultural machinery, influential factors were examined in this paper. The effects of these factors were quantified using logistic regression. Methods: Based on the results of a survey on farm-work accidents conducted by the National Academy of Agricultural Science, 21 tentative independent variables were selected. To apply these variables to regression, the presence of multicollinearity was examined by comparing correlation coefficients, checking the statistical significance of the coefficients in a simple linear regression model, and calculating the variance inflation factor. A logistic regression model and determination method of its goodness of fit was defined. Results: Among 21 independent variables, 13 variables were not collinear each other. The results of a logistic regression analysis using these variables showed that the model was significant and acceptable, with deviance of 714.053. Parameter estimation results showed that four variables (age, power tiller ownership, cognizance of the government's safety policy, and consciousness of safety) were significant. The logistic regression model predicted that the former two increased accident odds by 1.027 and 8.506 times, respectively, while the latter two decreased the odds by 0.243 and 0.545 times, respectively. Conclusions: Prevention strategies against factors causing an accident, such as the age of farmers and the use of a power tiller, are necessary. In addition, more efficient trainings to elevate the farmer's consciousness about safety must be provided.

• Fire Science and Engineering
• /
• v.28 no.2
• /
• pp.69-75
• /
• 2014

This study derives the factors which affect the occurrence of arson from statistical data (population, economic, and social factors) by multiple regression analysis. Multiple regression analysis applies to 4 forms of functions, linear functions, semi-log functions, inverse log functions, and dual log functions. Also analysis respectively functions by using the stepwise progress which considered selection and deletion of the independent variable factors by each steps. In order to solve a problem of multiple regression analysis, autocorrelation and multicollinearity, Variance Inflation Factor (VIF) and the Durbin-Watson coefficient were considered. Through the analysis, the optimal model was determined by adjusted Rsquared which means statistical significance used determination, Adjusted R-squared of linear function is scored 0.935 (93.5%), the highest of the 4 forms of function, and so linear function is the optimal model in this study. Then interpretation to the optimal model is conducted. As a result of the analysis, the factors affecting the arson were resulted in lines, the incidence of crime (0.829), the general divorce rate (0.151), the financial autonomy rate (0.149), and the consumer price index (0.099).