• 수산경영론집
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• 제46권1호
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• pp.93-107
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• 2015

This study aims to analyze causalities among Hairtail prices by distribution channel using a vector autoregressive model. This study applies unit-root test for stability of data, uses Granger causality test to know interaction among Hairtail Prices by distribution channel, and employes the vector autoregressive model to estimate statistical impacts among t-2 period variables used in model. Analyzing results of this study are as follows. First, ADF, PP, and KPSS tests show that the change rate of Hairtail price by distribution channel differentiated by logarithm is stable. Second, a Granger causality test presents that the producer price of Hairtail leads the wholesale price and then the wholesale price leads the consumer price. Third, the vector autoregressive model suggests that the change rate of Hairtail producer price of t-2 period variables statistically, significantly impacts change rates of own, wholesale, and consumer prices at current period. Fourth, the impulse response analysis indicates that impulse responses of the structural shocks with a respectively distribution channel of the Hairtail prices are relatively more powerful in own distribution channel than in other distribution channels. Fifth, a forecast error variance decomposition of the Hairtail prices points out that the own price has relatively more powerful influence than other prices.

• Communications for Statistical Applications and Methods
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• 제24권5호
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• pp.421-441
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• 2017

In this survey, estimation methods for structural vector autoregressive models are presented in a systematic way. Both frequentist and Bayesian methods are considered. Depending on the model setup and type of restrictions, least squares estimation, instrumental variables estimation, method-of-moments estimation and generalized method-of-moments are considered. The methods are presented in a unified framework that enables a practitioner to find the most suitable estimation method for a given model setup and set of restrictions. It is emphasized that specifying the identifying restrictions such that they are linear restrictions on the structural parameters is helpful. Examples are provided to illustrate alternative model setups, types of restrictions and the most suitable corresponding estimation methods.

• Ocean and Polar Research
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• 제36권4호
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• pp.327-341
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• 2014

This study aims to analyze causality among Wando abalone producer prices by size using a vector autoregressive model to expiscate the leading-price of Wando abalone in various price classes by size per kg. This study, using an analytical approach, applies a unit-root test for stability of data, a Granger causality test to learn about interaction among price classes by size for Wando abalone, and a vector autoregressive model to estimate the statistical impact among t-1 variables used in the model. As a result of our leading-price analysis of Wando abalone producer prices by shell size using a VAR model, first, DF, PP, and KPSS tests showed that the Wando abalone monthly price change rate by size differentiated by logarithm were stable. Second, the Granger causality relationship analysis showed that the price change rate for big size abalone weakly led the price change rate for the small and medium sizes of abalone. Third, the vector autoregressive model showed that three price change rates of t-1 period variables statistically, significantly impacted price change rates of own size and other sizes in t period. Fourth, the impulse response analysis indicated that the impulse responses of structural shocks for price change rate for big size abalone was relatively more powerful in its own size and in other sizes than shocks emanating from other sizes. Fifth, the variance decomposition analysis indicated that the price change rate for big size abalone was relatively more influential than the price change rates for medium and small size abalone.

• Journal of the Korean Statistical Society
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• 제36권4호
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• pp.501-522
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• 2007

We propose an estimation procedure that can be used for detecting structural changes in the seasonal cointegrated vector autoregressive model. The asymptotic properties of the estimates and the test statistics for the parameter change are provided. A simulation example is presented to illustrate this method and its concept.

• Smart Structures and Systems
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• 제17권6호
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• pp.1017-1030
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• 2016

This study focuses on the system identification of reinforced concrete bridges using vector autoregressive model (VAR). First, the time series output response from a bridge establishes the autoregressive (AR) models. AR models are one of the most accurate methods for stationary time series. Burg's algorithm estimates the autoregressive coefficients (ARCs) at p-lag by reducing the sum of the forward and the backward errors. The computed ARCs are assembled in the state system matrix and the eigen-system realization algorithm (ERA) computes: the eigenvector matrix that contains the vectors of the mode shapes, and the eigenvalue matrix that contains the associated natural frequencies. By taking advantage of the characteristic of the AR model with ERA (ARMERA), civil engineering can address problems related to damage detection. Operational modal analysis using ARMERA is applied to three experiments. One experiment is coupled with an artificial neural network algorithm and it can detect damage locations and extension. The neural network uses a specific number of ARCs as input and multiple submatrix scaling factors of the structural stiffness matrix as output to represent the damage.

• Smart Structures and Systems
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• 제26권1호
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• pp.23-33
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• 2020

Majority of the damage in engineering structures is nonlinear. Damage sensitive features (DSFs) extracted by traditional methods from linear time series models cannot effectively handle nonlinearity induced by structural damage. A new DSF is proposed based on vector space cosine similarity (VSCS), which combines K-means cluster analysis and Bayesian discrimination to detect nonlinear structural damage. A reference autoregressive moving average (ARMA) model is built based on measured acceleration data. This study first considers an existing DSF, residual standard deviation (RSD). The DSF is further advanced using the VSCS, and then the advanced VSCS is classified using K-means cluster analysis and Bayes discriminant analysis, respectively. The performance of the proposed approach is then verified using experimental data from a three-story shear building structure, and compared with the results of existing RSD. It is demonstrated that combining the linear ARMA model and the advanced VSCS, with cluster analysis and Bayes discriminant analysis, respectively, is an effective approach for detection of nonlinear damage. This approach improves the reliability and accuracy of the nonlinear damage detection using the linear model and significantly reduces the computational cost. The results indicate that the proposed approach is potential to be a promising damage detection technique.

• 응용통계연구
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• 제26권6호
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• pp.1043-1051
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• 2013

본 논문에서는, 구조적 오차수정모형을 한국의 노동시장 자료에 적용함으로써, 실업률에 미치는 구조적 충격의 영향을 분석한다. 이를 위하여 기술력, 노동수요, 노동공급, 임금 부문에서의 충격을 정의하였으며, 이를 각각 노동생산성, 취업자 수, 실업률, 실질임금과 연결하였다. 그 결과로서, 노동수요 및 노동공급 충격이 각각 장기적 및 단기적으로 실업률에 유의한 영향을 미치는 것으로 나타났다.

• 벤처창업연구
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• 제10권3호
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• pp.133-146
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• 2015

본 연구는 환헤지와 조선업체의 당기순이익과의 인과관계를 파악하고자 단위근 검정과 공적분 검정, 그리고 벡터자기회귀모형(vector autoregressive model : VAR)을 활용하였다. 먼저, 단위근 검정을 위해서는 2000년부터 2013년까지 분기별 조선업체들의 당기순이익은 존슨 변환 후의 값을 사용하였다. 동일 기간에 국채선물수익률(KTBF), 3년 만기 국채수익률(KTB3Y), 한미 환율, 한미 금리차이는 주별 데이터를 각 분기별 차이값으로 변환시켜서 활용하였고, 조선업체의 주가는 로그 변환 시킨 후에 사용하였다. 또한, 구조적 변화점 탐색 분석기법을 활용하여서 조선업체들의 당기순이익에 영향을 주는 환헤지에 구조적 전략 변화가 발생하는지 검증해 보았다. 연구결과는 환헤지와 조선업체의 당기순이익 간에는 구조적 변화점 탐지 분석에서는 2004년을 기점으로 구조적 변화가 발생하는 것으로 나타났다. 즉, 조선업체 중에서 소극적 환헤지 관리가 적극적 환헤지 전략으로 구조적 변화가 발생한 것이다. VAR의 추정을 통해 한국 조선업체들의 환헤지는 조선업체들의 상호 간의 수익성에 영향을 주고 있음을 파악 할 수 있었다. 또한 거시변수나 주가에 의해서도 조선업체들의 당기순이익이 영향을 받고 있음을 확인해 볼 수 있었다.

• 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.

• 통상정보연구
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• 제12권4호
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• pp.171-192
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• 2010

In general, a stock index and its individual stocks are assumed to follow a random walk. A stock index is an important source of information and one that is seen by people everyday, regardless of their investment intentions. This paper examines the correlation between the KOSPI-the index that best reflects the Korean stock market and the macro - economic variables that have been found to influence the index by previous studies. The sample period considers the years after 2000 when the Korean stock market matured as restrictions on foreign investors were removed. For this purpose, a Vector Error Correction Model (VECM) and KOSPI equation with a general pacific approach were used. This paper aims at verifying the factors that determined the KOSPI after 2000 and at examining whether there was structural change in the investment environment. It also investigates changes in the factors determining the KOSPI's performance as a result of structural changes in the investment environment. The V AR (Vector Autoregressive) model including the nine variables was selected as a baseline model whose stability was tested using the unit root test. The results from the VECM and the structural changes in the investment environment can be summarized by the following Inner story points.