• East Asian Economic Review
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• 제20권4호
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• pp.519-544
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• 2016

This paper examines quantile dependence and directional predictability between the foreign exchange market and the stock market in Korea. Instead of adopting a multivariate model such as a vector autoregressive model, a multivariate GARCH model or a combination of both models, we apply the cross-quantilogram recently proposed by Han et al. (2016). Considering various quantile ranges, we investigate various spillover effects between two markets. Our findings show that there exists an asymmetric bi-directional spillover between two markets and the interdependence between two markets implies that one market has significant predictive power on the other.

• 한국항만경제학회지
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• 제27권2호
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• pp.217-240
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• 2011

이 연구는 VAR 모형을 이용하여 국제유가가 BDI, 선형에 따라 BCI, BPI 등 3개의 운임 지수에 각각 어떠한 영향을 미치는지와 VECM모형을 이용하여 케이프사이즈와 파나막스 시장 간의 파급효과를 분석하였다. 첫째, VAR모형을 이용하여 국제유가의 변화가 BCI에 미치는 효과는 시차 1기의 경우 통계적으로 정(+)의 유의적인 효과를 갖고, BPI의 경우에는 시차 3기의 경우에만 음(-)의 유의적인 효과를 갖고, BDI 운임지수에 미치는 효과는 시차 1기의 경우 통계적으로 정(+)의 유의적인 효과를 갖는 것으로 나타났다. 충격반응함수 분석의 결과는 국제유가의 충격으로부터 BDI의 반응은 약 3개월 정도 지속적으로 상승하다가 이후로는 감소하는 것으로 나타났다. 둘째, VECM모형을 이용하여 케이프사이즈와 파나막스 시장 간의 파급효과를 분석한 결과는 BCI와 BPI 운임지수 간에 장기적인 균형관계로부터의 이탈이 발생하는 경우 BPI 운임지수가 감소하는 방향으로 조정되었다. 또한 동태적인 상관관계의 경우 시차 1기의 케이프사이즈 시장에서의 운임이 상승하면 금기의 파나막스 시장에서의 운임이 상승하는 것으로 나타났다. BCI와 BPI 운임지수간의 동학적인 충격반응함수의 분석으로부터 BCI 운임지수의 충격으로부터 BPI 운임지수의 반응은 약 3개월 정도 가파르게 상승하다가 5개월 이후로는 변화가 없는 것으로 나타났고, BPI 운임지수의 충격에 대한 BCI 운임지수의 충격반응의 정도는 매우 작게 나타났으며, 약 3개월 정도 완만하게 상승하다가 이후로 거의 변화가 없는 것으로 나타났다.

• Communications for Statistical Applications and Methods
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• 제22권5호
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• pp.415-434
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• 2015

In this paper we overview the literature on common features analysis of economic time series. Starting from the seminal contributions by Engle and Kozicki (1993) and Vahid and Engle (1993), we present and discuss the various notions that have been proposed to detect and model common cyclical features in macroeconometrics. In particular, we analyze in details the link between common cyclical features and the reduced-rank regression model. We also illustrate similarities and differences between the common features methodology and other popular types of multivariate time series modelling. Finally, we discuss some recent developments in this area, such as the implications of common features for univariate time series models and the analysis of common autocorrelation in medium-large dimensional systems.

• Journal of Information Processing Systems
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• 제17권6호
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• pp.1044-1056
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• 2021

Globalization has integrated the world through interaction among countries and people with the help of information and telecommunication technology (ICT). The rapid mode of globalization has put a new life in ICT and economic sector. The key focus of this study is to examine the nexus among the globalization, ICT and economic growth. This study uses autoregressive distributed lag model (ARDL), vector error correction model (VECM) and econometric method spanning from 1990 to 2015. The empirical result highlights that the globalization stimulates economic growth of a country. In addition, both the internet penetration and the mobile phone usage contribute to the economic growth. Lastly, this article contributes important policy lessons on strengthening the economy by utilizing ICT with the rapid globalization.

• Smart Structures and Systems
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• 제32권1호
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• pp.61-81
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• 2023

This study presents an ensemble learning based Bayesian model updating approach for structural damage diagnosis. In the developed framework, the structure is initially decomposed into a set of substructures. The autoregressive moving average (ARMAX) model is established first for structural damage localization based structural motion equation. The wavelet packet decomposition is utilized to extract the damage-sensitive node energy in different frequency bands for constructing structural surrogate models. Four methods, including Kriging predictor (KRG), radial basis function neural network (RBFNN), support vector regression (SVR), and multivariate adaptive regression splines (MARS), are selected as candidate structural surrogate models. These models are then resampled by bootstrapping and combined to obtain an ensemble model by probabilistic ensemble. Meanwhile, the maximum entropy principal is adopted to search for new design points for sample space updating, yielding a more robust ensemble model. Through the iterations, a framework of surrogate ensemble learning based model updating with high model construction efficiency and accuracy is proposed. The specificities of the method are discussed and investigated in a case study.

• 산경연구논집
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• 제8권4호
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• pp.15-21
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• 2017

Purpose - In this work, we examined the causal relationship between credit loans from households (CLH), loan collateralized with housing (LCH) and an interest of certificate of deposit (ICD) among others in South Korea. Furthermore, the optimal forecasts on the underlying model will be obtained and have the potential for applications in the economic field. Research design, data, and methodology - A total of 31 realizations sampled from the 4th quarter in 2008 to the 4th quarter in 2016 was chosen for this research. To achieve the purpose of this study, a regression model with correlated errors was exploited. Furthermore, goodness-of-fit measures was used as tools of optimal model-construction. Results - We found that by applying the regression model with errors component ARMA(1,5) to CLH, the steep and lasting rise can be expected over the next year, with moderate increase of LCH and ICD. Conclusions - Based on 2017-2018 forecasts for CLH, the precipitous and lasting increase can be expected over the next two years, with gradual rise of two major explanatory variables. By affording the assumption that the feedback among variables can exist, we can, in the future, consider more generalized models such as vector autoregressive model and structural equation model, to name a few.

• Journal of Electrical Engineering and Technology
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• 제13권3호
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• pp.1089-1098
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• 2018

This study aims at developing and applying a hybrid model to the wind power prediction (WPP). The hybrid model for a very-short-term WPP (VSTWPP) is achieved through analytical data, multiple linear regressions and least square methods (MLR&LS). The data used in our hybrid model are based on the historical records of wind power from an offshore region. In this model, the WPP is achieved in four steps: 1) transforming historical data into ratios; 2) predicting the wind power using the ratios; 3) predicting rectification ratios by the total wind power; 4) predicting the wind power using the proposed rectification method. The proposed method includes one-step and multi-step predictions. The WPP is tested by applying different models, such as the autoregressive moving average (ARMA), support vector machine (SVM), and artificial neural network (ANN). The results of all these models confirmed the validity of the proposed hybrid model in terms of error as well as its effectiveness. Furthermore, forecasting errors are compared to depict a highly variable WPP, and the correlations between the actual and predicted wind powers are shown. Simulations are carried out to definitely prove the feasibility and excellent performance of the proposed method for the VSTWPP versus that of the SVM, ANN and ARMA models.

• 수산경영론집
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• 제37권1호
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• pp.95-110
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• 2006

Japan's frozen shrimp future market is the only fisheries future commodity market in the world. This empirical study examines the lead and lag relationship between Japan frozen shrimp spot and future markets using the daily prices from August 1, 2002 to December 31, 2005. Frozen shrimp future contract is listed on Japan Kansai Commodities Exchange. Japan imports approximately 250,000 tons of frozen shrimp annually, of which just under 70,000 tons, nearly 30%, are black tiger shrimp. Approximately 90% of black tiger shrimp are caught in Indonesia, India, Thailand and Vietnam, and the two largest consumers of these shrimp are Japan and the U.S.A. Kansai Commodities Exchange adopts the India black tiger shrimp as standard future commodity. We use unit root test, Johansen cointegration test, Granger causality test, Vector autoregressive analysis and Impulse response analysis. However, considering the long - term relationships between the level variables of frozen shrimp spot and futures, we introduced Vector Error Correction Model. We find that the price change of frozen shrimp futures with next 1, 2, 3, 4, 5 month maturity have a strong predictive power to the change of frozen shrimp spot and the change of frozen shrimp spot also have a predictive power to the change of frozen shrimp with next 1, 2, 3 month maturity. But, the explanatory power of the frozen shrimp futures is relatively greater than that of frozen shrimp spot.

• 지식경영연구
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• 제19권2호
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• pp.1-19
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• 2018

This study aims to identify the causal relationship among public technology transfer, technological performance, and research and development (R&D) productivity. Using the impulse-response function(IRF) of a panel vector autoregressive model (panel VAR), this study suggests the results of how long the factors such as technological performance (patent), public technology transfer, and R&D productivity takes and lasts if a one-unit shock of standard deviation occurs. As a result, first, the increase of public technology transfer activities has no power to increase the technology performance but improve the R&D productivity. If the public institute increases its technology transfer activities by one unit, the R&D productivity will increase within five years. Second, the impact of increasing technological performance on improvement of public technology transfer and R&D productivity is an insignificant. Third, the effect of R&D productivity on the public technology transfer creates a substantial reaction after a current time. Considering the structural relationships among public technology transfer, technological performance, and R&D productivity, if policy makers intend to construct the active R&D circumstance, technology suppliers should be motivated to run the active R&D mechanism because they achieve gains.

• Journal of Korea Trade
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• 제25권1호
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• pp.18-33
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• 2021

Purpose - This paper investigates the causal relationship between logistics infrastructure development and the economic growth of Korea. Considering the industrial and economic structure of Korea, it is likely that logistics infrastructure is positively associated with the economic growth of the country. Design/methodology - The causal relationship between logistics infrastructure and economic development is estimated using Vector Autoregressive (VAR) and Vector Error Correction Model (VECM) considering long-run equilibrium between the two factors. To this end, a dataset consisting of 7 logistics infrastructure proxies and 5 economic growth indicators covering the period of 1990-2017 is used. Findings - It was found that causality, in general, runs from logistics infrastructure development to economic growth. Specifically, the results indicate that maritime transport is positively associated with the economic growth of Korea in terms of GDP and international trade. In addition, other modes of transport also have a positive impact on either the GDP or international trade of Korea. Originality/value - While existing studies in this area are based on either regional observations or a specific mode of transport, this study presents empirical evidence on causality between logistics infrastructure and the economic growth of Korea using a more comprehensive dataset. In addition, the findings in this paper can provide valuable implications for transport infrastructure development policies.