• International Area Studies Review
• /
• v.22 no.4
• /
• pp.19-35
• /
• 2018

This paper is to estimate the total factor productivity(TFP) in the Korean manufacturing and service industries during the period 1975:1-2016:4 using the stochastic frontier analysis model. In order to analyze the determinants for the total factor productivity the paper estimates the industry-specific determinant elasticities of TFP using the autoregressive distributed model. The industry-specific determinants, which reflect the industrial structure and properties include markup, the ratio of capital to labor(KL), and the ratio of foreign intermediate goods (FIG) to industrial output. The average value for total factor productivity growth was estimated to be 0.0199 in manufacturing and 0.0063 in the service industry. The markup and KL elasticities of TFP were estimated to be 2.481 and 0.651 in manufacturing respectively and -1.403 and 0.042 in the service industry respectively. The empirical results suggest that the industrial markup and the ratio of capital to labor have had decisive effects on the changes in the total factor productivity in the Korean manufacturing and service industries during the period 1975:1-2016:4.

• International Area Studies Review
• /
• v.20 no.1
• /
• pp.3-22
• /
• 2016

In this study, I examine mutual shock spillover effects among interest rate differences, won-dollar foreign exchange change rates, and stock market returns in Korea during the daily sample period from the beginning of 1995 to the October 16, 2015, using the multivariate GARCH (generalized autoregressive conditional heteroscedasticity) BEKK (Baba-Engle-Kraft-Kroner) model framework. Major findings are as follows. Throughout the 6 model estimation results of variance equations determining return spillovers covered from symmetric and asymmetric models of total sample period and two crisis sub-sample periods composed of Korean FX Crisis Times and Global Financial Crisis Times, shock spillovers are shown to exist mainly from stock market return shocks. Stock market shocks including down-shocks from the asymmetric models are shown to transfer to those other two markets most successfully. Therefore it is most important to maintain stable financial markets that a policy design for stock market stabilization such as mitigating stock market volatility.

• Journal of the Korean Solar Energy Society
• /
• v.39 no.3
• /
• pp.59-66
• /
• 2019

New and renewable energy forecasts are key technology to reduce the annual operating cost of new and renewable facilities, and accuracy of forecasts is paramount. In this study, we intend to build a model for the prediction of short-term solar power generation for 1 hour to 3 hours. To this end, this study applied two time series technique, ARIMA model without considering seasonality and SARIMA model with considering seasonality, comparing which technique has better predictive accuracy. Comparing predicted errors by MAE measures of solar power generation for 1 hour to 3 hours at four locations, the solar power forecast model using ARIMA was better in terms of predictive accuracy than the solar power forecast model using SARIMA. On the other hand, a comparison of predicted error by RMSE measures resulted in a solar power forecast model using SARIMA being better in terms of predictive accuracy than a solar power forecast model using ARIMA.

• The Korean Journal of Applied Statistics
• /
• v.32 no.1
• /
• pp.69-81
• /
• 2019

We consider bivariate long range dependent (LRD) time series forecasting using a deep learning method. A long short-term memory (LSTM) network well-suited to time series data is applied to forecast bivariate time series; in addition, we compare the forecasting performance with bivariate fractional autoregressive integrated moving average (FARIMA) models. Out-of-sample forecasting errors are compared with various performance measures for functional MRI (fMRI) data and daily realized volatility data. The results show a subtle difference in the predicted values of the FIVARMA model and VARFIMA model. LSTM is computationally demanding due to hyper-parameter selection, but is more stable and the forecasting performance is competitively good to that of parametric long range dependent time series models.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.61 no.1
• /
• pp.61-74
• /
• 2019

In this study, the SPI (Standardized Precipitation Index) of meteorological drought and SDI (Streamflow Drought Index) of hydrological drought for 1, 3, 6, 9, and 12 months duration were estimated to analyse the characteristics of drought using rainfall and dam inflow data for Chungju dam ($6,661.8km^2$) with 31 years (1986-2016) and Boryeong dam ($163.6km^2$) watershed with 19 years (1998-2016) respectively. Using the estimated SPI and SDI, the drought forecasting was conducted using seasonal autoregressive integrated moving average (SARIMA) model for the 5 durations. For 2016 drought, the SARIMA had a good results for 3 and 6 months. For the 3 months SARIMA forecasting of SPI and SDI, the correlation coefficient of SPI3, SPI6, SPI12, SDI1, and SDI6 at Chungju Dam showed 0.960, 0.990, 0.999, 0.868, and 0.846, respectively. Also, for same duration forecasting of SPI and SDI at Boryeong Dam, the correlation coefficient of SPI3, SPI6, SDI3, SDI6, and SDI12 showed 0.999, 0.994, 0.999, 0.880, and 0.992, respectively. The SARIMA model showed the possibility to provide the future short-term SPI meteorological drought and the resulting SDI hydrological drought.

• The Journal of Asian Finance, Economics and Business
• /
• v.8 no.3
• /
• pp.751-761
• /
• 2021

The Vietnamese economy is a developing country that has brought many opportunities and challenges for the banking system. Commercial banks have developed strongly from quality to quantity, which plays a vital role in developing the economy. They play an important role in capital formation, which is essential for the economic development of a country. They provide financial services to the general public and businesses, ensuring economic and social stability and sustainable growth of the economy. Therefore, the relationship between bank depth and economic growth is of importance in research. This paper used a VAR (Vector Autoregressive Models) estimator for time series data models. The data is collected quarterly from the first quarter of the year 2000 to 2020. The study uses the VAR model to examine the causal relationships of economic growth, growth in money supply expansion, private sector capital requirement, and banks' domestic credit. The results indicate a general short-run relationship between banking sector depth and economic growth with a positive connection, but in the long term, the relationship between these variables can be reversed because of other macro factors. The findings show the two-way causal relationship between GDP growth and banking depth factors. This research contributes to policy-making by underlining the banking sector depth determinants when setting regulations and policies to develop the banking sector.

• International Area Studies Review
• /
• v.16 no.1
• /
• pp.3-27
• /
• 2012

This paper adopts recently developed panel unit root test that is cross-sectionally robust. Cointegration test is also used to find whether regional house prices are in line with gross regional domestic production (GRDP) in the long run in Korea during 1989-2009. Based on the panel VECM and the panel ARDL models, we examine causal relationships among the variables and estimate the long-run elasticity. We find evidence of cointegration and bidirectional causal relationships between regional house prices and GRDP. The results of long-run estimates, using both fixed effect and ARDL models, show that house prices positively and significantly influence on the GRDP and vice versa. Together with these results, the findings of ARDL-ECM imply that there exists a long-run equilibrium relationship between house prices and regional economic variables even if there is a possibility of short-run deviation from its long-run path.

• International Area Studies Review
• /
• v.21 no.4
• /
• pp.3-17
• /
• 2017

The purpose of the paper is to suggest the empirical evidences for the effects of factor prices on the export performance in the Korean manufacturing industries during the period 1975:1-2016:4. The paper is to set up the error correction model derived from the autoregressive distributed lag scheme and to estimate the factor price elasticities of export in the 8 manufacturing industries. The real wage, interest and import price index elasticities of export all were estimated to be statistically significant at 1% level in the most industries with showing negative signs as expected. And the real wage elasticity proved to likely be smaller as the industries become more capital-intensive while the import price index elasticity tended to become larger in industries with larger ratio of imported intermediate goods to output. The empirical results suggest that the declines in input factor prices since the foreign exchange crisis in the end of 1997 have positive effects on the export performance in the Korean manufacturing industries.

• The Korean Journal of Applied Statistics
• /
• v.35 no.3
• /
• pp.395-406
• /
• 2022

This paper studies the outlier detection method for multivariate long memory time series. The existing outlier detection methods are based on a short memory VARMA model, so they are not suitable for multivariate long memory time series. It is because higher order of autoregressive model is necessary to account for long memory, however, it can also induce estimation instability as the number of parameter increases. To resolve this issue, we propose outlier detection methods based on the VHAR structure. We also adapt the robust estimation method to estimate VHAR coefficients more efficiently. Our simulation results show that our proposed method performs well in detecting outliers in multivariate long memory time series. Empirical analysis with stock index shows RVHAR model finds additional outliers that existing model does not detect.

• The Journal of the Acoustical Society of Korea
• /
• v.41 no.4
• /
• pp.468-479
• /
• 2022

To detect targets with active sonar system in the underwater environments, the targets are localized by receiving the echoes of the transmitted sounds reflected from the targets. In this case, reverberation from the scatterers is also generated, which prevents detection of the target echo. To detect the target effectively, reverberation suppression techniques such as pre-whitening based on autoregressive model and principal component inversion have been studied, and recently a Non-negative Matrix Factorization (NMF)-based technique has been also devised. The NMF-based reverberation suppression technique shows improved performance compared to the conventional methods, but the geometry of the transducer and receiver and attenuation by distance have not been considered. In this paper, the performance is improved through preprocessing such as the directionality of the receiver, Doppler related thereto, and attenuation for distance, in the case of using a continuous wave with a bistatic sonar. In order to evaluate the performance of the proposed system, simulation with a reverberation model was performed. The results show that the detection probability performance improved by 10 % to 40 % at a low false alarm probability of 1 % relative to the conventional non-negative matrix factorization.