• The Journal of Asian Finance, Economics and Business
• /
• v.7 no.2
• /
• pp.143-149
• /
• 2020

Low inflation and sustainable growth have been the major macroeconomic goals being pursued by every developing country, Vietnam inclusive. The effect of inflation on economic growth has been intensively analyzed by a variety of studies, but the empirical evidence more often than not remains controversial and ambiguous. One common hypothesis of previous studies is that they have assumed that the effect of inflation on growth is symmetric. The main purpose of this study is to investigate the asymmetric effect of inflation and money supply on economic growth using the Nonlinear Autoregressive Distributed Lag approach introduced by Shin, Byungchul, and Greenwood-nimmo (2013) for Vietnam over the period 1990-2017. Empirical results provide evidence that the effects of inflation on economic growth are negative and asymmetric in the long run. The impact of money supply on growth is positive in both the short-run and long-run. Accordingly, the impact of the increase in the inflation rate is bigger than the decreasing in the long-run. This different impact is significant and high inflation will destruct economic activities. As a result, the study provides empirical evidence for the authorities to plan monetary policies and control the rate of inflation to achieve sustainable economic development in the long-run.

• The Journal of Asian Finance, Economics and Business
• /
• v.2 no.2
• /
• pp.5-12
• /
• 2015

The authors calculate the long-term predictability of GDP, domestic demand, investment, and net exports for Guangdong province, P.R. China from 2000 to 2013. A vector autoregressive (VAR) model with quarterly data for this period is first co-integrated then the Granger causality test is applied to empirically assess the relationships among gross domestic product (GDP), consumption, investment, and net exports. There is a strong causality effect between investment and net exports in Guangdong province. However, the variance decomposition results indicate that exports respond to foreign shocks rather than domestic ones, making their impact on the Guangdong economy to predict. Results show the stimulating effect of domestic demand on GDP is larger than the stimulating effect of net exports and much larger than even the stimulating effect of investment. The analysis suggests that there are dynamic influences with various levels of persistence between GDP, consumption, investment, and net exports. Macroeconomic policy adjustments are urgently required to expand domestic demand and thereby stimulate economic growth in Guangdong province.

• The Journal of Asian Finance, Economics and Business
• /
• v.8 no.11
• /
• pp.203-212
• /
• 2021

Theoretically, economic growth necessitates financial liberalization. Thus, the current research examines the effect of financial liberalization on economic growth in emerging nations, with a particular focus on Egypt and Saudi Arabia. To determine this effect, the study employs a model that uses Gross Domestic Product growth as the dependent variable and the following macroeconomic variables as financial liberalization indices: Broad money as a percentage of GDP, Domestic bank credit to the private sector as a percentage of GDP, Monetary sector credit to the private sector as a percentage of GDP, Net inflows of foreign direct investment as a percentage of GDP. All data is annual data of Egypt and the Kingdom of Saudi Arabia for the period 1970-2018 obtained from the World Bank open data website. The empirical investigation employs the Autoregressive Distributed Lag (ARDL) approach. The findings indicate that, after more than three decades of implementation, both countries' financial and external liberalization policies do not have a favorable effect on their economies' growth rates. Additionally, this study has led us to conclude that any financial liberalization policy in both countries must be preceded by the strengthening of these countries' financial development and institutional frameworks, as well as the achievement of macroeconomic stability.

• Journal of the Korean Data and Information Science Society
• /
• v.25 no.1
• /
• pp.155-167
• /
• 2014

This study uses a continuous autoregressive (CAR) model to analyze daily average temperature in six Korean metropolitan cities. Data period is Jan. 1, 1954 to Dec. 31, 2010 covering 57 years. Using a relative long time series reveals that the linear time trend components are all statistically significant in the six cities, which was not shown in previous studies. Particularly the plus sign of its coefficient implies the effect on Korea of the global warming. Unit-root test results are that the temperature time series are stationary without unit-root. It turns out that CAR(3) is suitable for stochastic component of the daily temperature. Since developing suitable continuous stochastic model of the underlying weather related variables is crucial in pricing the weather derivatives, the results in this study will likely prove useful in further future studies on pricing weather derivatives.

• Journal of Digital Convergence
• /
• v.13 no.8
• /
• pp.267-279
• /
• 2015

The study aimed to examine the longitudinal relationship between addictive use of mobile phones and learning activities for elementary school students and to analyze (1)temporal changes of the addictive use of mobile phones and learning activities, (2)temporal relationship between them, and (3)multiple and complex group analysis across gender on the relationship. For the study, 3-year longitudinal data(2010-2012) of the Korean Children and Youth Panel Survey and autoregressive cross-lagged modeling were used. The findings showed that the addictive use of mobile phones and learning activities had a significant positive effect on the future selves of children over time. The addictive use of mobile phones influenced positively subsequent learning activities, but, the learning activities did not affect the addictive use of mobile phones. Further, there were no significant gender differences in the longitudinal relationship. The study provided the useful data to make guidelines on how to protect mobile phone addiction for elementary school students.

• The Korean Journal of Applied Statistics
• /
• v.28 no.3
• /
• pp.429-442
• /
• 2015

Volatility forecasting in financial markets is an important issue because it is directly related to the profit of return. The volatility is generally modeled as time-varying conditional heteroskedasticity. A generalized autoregressive conditional heteroskedastic (GARCH) model is often used for modeling; however, it is not suitable to reflect structural changes (such as a financial crisis or debt crisis) into the volatility. As a remedy, we introduce the Markov regime switching GARCH (MRS-GARCH) model. For the empirical example, we analyze and forecast the volatility of the daily Korea Composite Stock Price Index (KOSPI) data from January 4, 2000 to October 30, 2014. The result shows that the regime of low volatility persists with a leverage effect. We also observe that the performance of MRS-GARCH is superior to other GARCH models for in-sample fitting; in addition, it is also superior to other models for long-term forecasting in out-of-sample fitting. The MRS-GARCH model can be a good alternative to GARCH-type models because it can reflect financial market structural changes into modeling and volatility forecasting.

• Economic and Environmental Geology
• /
• v.50 no.4
• /
• pp.267-276
• /
• 2017

Seawater intrusion into coastal fractured rock aquifer, resulting in groundwater contamination, is of serious concern in coastal areas of Jeolla Namdo, Korea, which heavily depends on groundwater resources. Time series analysis and forecasting were carried out to analyze and predict EC which is a major indicator of seawater intrusion. Two time series models of autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA) were tested for suggesting appropriate time series model. Time series data of EC measured over one year showed a increasing trend with short periodic fluctuations, due to tidal effect and pumping, which indicated that EC time series data tended to be non-stationary. SARIMA model was found better fitted to observed EC than any other time series model. Time series analysis and modeling was found to be a useful tool to analyze EC at coastal fractured rock aquifer subject to seawater intrusion.

• Journal of Preventive Medicine and Public Health
• /
• v.44 no.6
• /
• pp.242-248
• /
• 2011

Objectives: Busan is reported to have the highest mortality rate among 16 provinces in Korea, as well as considerable health inequality across its districts. This study sought to examine overall and cause-specific mortality and deprivation at the town level in Busan, thereby identifying towns and causes of deaths to be targeted for improving overall health and alleviating health inequality. Methods: Standardized mortality ratios (SMRs) for all-cause and four specific leading causes of death were calculated at the town level in Busan for the years 2005 through 2008. To construct a deprivation index, principal components and factor analysis were adopted, using 10% sample data from the 2005 census. Geographic information system (GIS) mapping techniques were applied to compare spatial distributions between the deprivation index and SMRs. We fitted the Gaussian conditional autoregressive model (CAR) to estimate the relative risks of mortality by deprivation level, controlling for both the heterogeneity effect and spatial autocorrelation. Results: The SMRs of towns in Busan averaged 100.3, ranging from 70.7 to 139.8. In old inner cities and towns reclaimed for replaced households, the deprivation index and SMRs were relatively high. CAR modeling showed that gaps in SMRs for heart disease, cerebrovascular disease, and physical injury were particularly high. Conclusions: Our findings indicate that more deprived towns are likely to have higher mortality, in particular from cardiovascular disease and physical injury. To improve overall health status and address health inequality, such deprived towns should be targeted.

• Journal of Korean Academy of Nursing
• /
• v.49 no.1
• /
• pp.69-79
• /
• 2019

Purpose: This study aimed to identify the directionality of the causal relationship and interaction between depression and amount of smoking over time in hardcore smokers using longitudinal descriptive analysis. Methods: Secondary data from the Korean Welfare Panel Study were analyzed using autoregressive cross-lagged modeling. Participants included 342 hardcore smokers who participated in the 8th to 11th waves of the panel study. Results: Analyses revealed that change(s) in depression levels according to time had a significant positive relationship with the total amount of smoking per day (${\beta}=.29$, ${\beta}=.19$, ${\beta}=.17$, p<.001), while change(s) in total amount of smoking per day according to time had a significant positive relationship with depression (${\beta}=.43$, ${\beta}=.50$, ${\beta}=.38$, p<.001). Analysis of the cross-lagged effect between depression and total amount of smoking per day showed that depression at one time point had a significantly positive relationship with the total amount of smoking per day at the next time point (${\beta}=.14$, ${\beta}=.13$, ${\beta}=.13$, p=.021), and that the total amount of smoking per day at one time point had a significant positive relationship with depression at the next time point (${\beta}=.04$, ${\beta}=.04$, ${\beta}=.03$, p=.044). Conclusion: The findings in the present study confirmed a cross-interaction between depression and total amount of smoking per day in hardcore smokers. The present findings could be used to develop appropriate smoking-related interventions.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.24 no.11
• /
• pp.1492-1499
• /
• 2020

A weather radar system generally shows the weather phenomena related with rainfall and wind velocity. These systems are usually very helpful to monitor the relatively high altitude weather situation for the wide and long range area. However, since the weather hazards due to the strong hail and heavy rainfall occurring locally are observed frequently in recent days, it is important to detect these wether phenomena. For this purpose, it is necessary to detect the fast varying low altitude weather conditions. In this environment, the effect of surface clutter is more evident and the antenna dwell time is much shorter. Therefore, the conventional Doppler spectrum estimation method may cause serious problems. In this paper, the AR(autoregressive) Doppler spectrum estimation methods were applied to solve these problems and the results were analyzed. Applied methods show that improved Doppler spectra can be obtained comparing with the conventional FFT(Fast Fourier Transform) method.