• 응용통계연구
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• 제36권6호
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• pp.529-545
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• 2023

본 논문에서는 장기 기억성을 가지는 고차원 시계열 데이터 분석에 유용한, 밴드 구조의 계수행렬들을 가지는 밴드구조 VHAR (Banded-VHAR) 모형을 제안한다. 밴드구조 VHAR 모형은 인접한 차원의 시계열에서만 상관구조를 가지는 성근 고차원 시계열 모형으로 밴드구조에 영향을 주는 요인으로는 대표적으로 지리적 특성이 있다. 밴드구조 VHAR 모형의 빠른 추정을 위해 본 논문은 행별추정방법을 사용하고 또 밴드의 크기를 추정하기 위해 BIC와 잔차제곱합의 비율을 이용한 추정 방법을 소개하였다. 더불어 모의 실험을 통해서 제안한 추정 방법의 점근적 일치성을 확인하였다. 실증자료 분석으로 지역별 초미세먼지 및 아파트 거래량 자료를 활용하여 모형을 적용한 결과 밴드구조 VHAR 모형이 표본외예측 능력의 우수하고, 지리적정보에 기반하여 모형의 해석이 용이하다는 큰 장점이 있음을 살펴보았다.

• 자원ㆍ환경경제연구
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• 제12권4호
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• pp.687-708
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• 2003

VAR(벡터자기회귀)에서 모형의 식별가정에 관한 주된 비판은 변수의 나열순서에 따라 결과가 달라진다는 것이다. 본 논문은 Swanson and Granger (1997) 이후 시계열 분석에 활발히 적용되기 시작한 그래프이론이 이와 같은 임의식별 문제를 해결함으로써, 자원가격의 가격발현과정을 이해하는데 유용한 수단임을 보여준다. 모형이 이론적 방법론을 소개한 후, 미국 대두의 지역 베이시스를 이용한 실증추정 결과를 제시한다.

• 동아시아경상학회지
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• 제1권1호
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• pp.8-16
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• 2013

Purpose: The objective of this paper is to identify and assess the contribution of budgetary, monetary and institutional shocks affecting the Tunisian economy over the period 1976-2003. The methodology used is vector autoregressive models and structural recent techniques for the analysis of time series related. The empirical results show a significant relationship between the supply shock and institutions on the one hand, and between institutional shocks and economic activity on the other hand. Research Design, Data and Methodology: As part of this section we will try to identify and assess the contribution of various shocks to macroeconomic variables' fluctuations for the Tunisian economy. The study period is: 1976-2003 and observations are annual. Results: The real business cycle theory argues that fluctuations in aggregate economic activity are the result of the interaction of the only real factors namely agents' preferences, technological opportunities, factor endowments and possibly certain institutional constraints. Conclusions: The lowest contribution to the variability of these rights is the monetary shock. As for "civil liberties", the largest share of their variability is the shock relating to the "political rights" during the first four periods .

• Communications for Statistical Applications and Methods
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• 제22권6호
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• pp.615-624
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• 2015

We consider the estimation of seasonal cointegration in the presence of conditional heteroskedasticity (CH) using a feasible generalized least squares method. We capture cointegrating relationships and time-varying volatility for long-run and short-run dynamics in the same model. This procedure can be easily implemented using common methods such as ordinary least squares and generalized least squares. The maximum likelihood (ML) estimation method is computationally difficult and may not be feasible for larger models. The simulation results indicate that the proposed method is superior to the ML method when CH exists. In order to illustrate the proposed method, an empirical example is presented to model a seasonally cointegrated times series under CH.

• East Asian Economic Review
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• 제22권3호
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• pp.371-400
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• 2018

Although asset price is an important factor in determining changes in external balances, no studies have investigated it from the Chinese perspective. In this study, I empirically examine the underlying driving forces of China's trade balances, particularly the role of asset price and the real exchange rate. To this end, I estimate a sign-restricted structural vector autoregressive model with quarterly time series data for China, using the Bayesian method. The results show that changes in asset price affect China's trade balances through private consumption and investment. Also, an appreciation of the real exchange rate tends to deteriorate trade balances in China. Furthermore, forecast error variance decomposition results indicate that changes in asset price (stock price and housing price) explain about 20% variability of trade balances, while changes in the real exchange rate can explain about 10%.

• 대한산업공학회지
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• 제27권3호
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• pp.289-296
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• 2001

This paper proposes multivariate control chart for autocorrelated data which are common in chemical and process industries and lead to increase in the number of false alarms when conventional control charts are applied. The effect of autocorrelated data is modeled as a vector autoregressive process, and canonical analysis is used to reduce the dimensionality of the data set and find the canonical variables that explain as much of the data variation as possible. Charting statistics are constructed based on the residual vectors from the canonical variables which are uncorrelated over time, and therefore the control charts for these statistics can attenuate the autocorrelation in the process data. The charting procedures are illustrated with a numerical example and Monte Carlo simulation is conducted to investigate the performances of the proposed control charts.

• 한국소음진동공학회논문집
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• 제13권1호
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• pp.48-55
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• 2003

Hidden Markov Model(HMM) has a doubly embedded stochastic process with an underlying stochastic process that can be observed through another set of stochastic processes. This structure of HMM is useful for modeling vector sequence that doesn't look like a stochastic process but has a hidden stochastic process. So, HMM approach has become popular in various areas in last decade. The increasing popularity of HMM is based on two facts : rich mathematical structure and proven accuracy on critical application. In this paper, we applied continuous HMM (CHMM) approach with AR coefficient to detect and predict the chatter of lathe bite and to diagnose the wear of oil Journal bearing using rotor shaft displacement. Our examples show that CHMM approach is very efficient method for machine health monitoring and prediction.

• 품질경영학회지
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• 제28권2호
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• pp.57-69
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• 2000

This paper discusses the local influence approach to the linear regression models with AR(2) errors. Diagnostics for the linear regression models with AR(2) errors are proposed and developed when simultaneous perturbations of the response vector are allowed- That is, the direction of maximum curvature of local influence analysis is obtained by studying the curvature of a surface associated with the overall discrepancy measure.

• Journal of Electrical Engineering and information Science
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• 제2권6호
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• pp.20-27
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• 1997

We present a method of electromyographic(EMG) pattern recognition to identify motion commands for the control of a prosthetic arm by evidence accumulation with multiple parameters. Integral absolute value, variance, autoregressive(AR) model coefficients, linear cepstrum coefficients, and adaptive cepstrum vector are extracted as feature parameters from several time segments of the EMG signals. Pattern recognition is carried out through the evidence accumulation procedure using the distances measured with reference parameters. A fuzzy mapping function is designed to transform the distances for the application of the evidence accumulation method. Results are presented to support the feasibility of the suggested approach for EMG pattern recognition.

• 한국산림과학회지
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• 제96권2호
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• pp.222-226
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• 2007

This study developed the Korean roundwood import prediction model using vector autoregressive (VAR) method. The roundwood was divided into softwood and hardwood by species. The VAR model of roundwood import was specified with two lagged endogenous variables, that is, roundwood import volume and roundwood import price. The results showed that the significance levels of F-statistics in the softwood and hardwood roundwood import equations rejected the hypothesis that all coefficients are zero. So, we concluded that roundwood import volume can be explained by lagged import volume and lagged import price in Korea. The coefficient signs of all variables were as expected. Also, the model has good explanatory power, and there is no autocorrelation.