• 제목/요약/키워드: Vector Autoregressive Model

검색결과 137건 처리시간 0.021초

Impact of Financial Instability on Economic Activity: Evidence from ASEAN Developing Countries

  • TRAN, Tra Thi Van
    • The Journal of Asian Finance, Economics and Business
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    • 제9권1호
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    • pp.177-187
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    • 2022
  • Theoretical literature agrees on the interaction between financial instability and economic activity but explains it's dynamic in two points of view: one is that the transmission mechanism occurs in one unique regime and the other reckons a shift of regime leads to the alteration of the transmission mechanism. This study aims to find evidence of the multi-regime transmission for ASEAN developing countries. The author employs the technique of Threshold vector auto regression using the financial stress index standing for financial instability. Monthly data is collected, covering a period long enough with many episodes of high stress in recent decades. There are two conclusions: (1) A financial shock has a negative and stronger impact on economic activity during a high-stress period than it does during a low-stress period; (2) the response of economic activity to a negative financial shock during high-stress periods is stronger than it is during normal times. The findings point to the importance of the financial stress index as an additional early warning indicator for the real economy sector, as well as the positive effect that a reduction in financial stress may have on economic activity, implying the importance of "unconventional" monetary policy in times of high financial stress.

The Impact of the Regional Comprehensive Economic Partnership (RCEP) on Intra-Industry Trade: An Empirical Analysis Using a Panel Vector Autoregressive Model

  • Guofeng Zhao;Cheol-Ju Mun
    • Journal of Korea Trade
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    • 제27권3호
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    • pp.103-118
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    • 2023
  • Purpose - This study aims to examine the dynamic relationship between the variables impacted by the Regional Comprehensive Economic Partnership (RCEP) and the level of intra-industry trade among member states, with the ultimate objective of deducing the short- and long-term effects of RCEP on trade. Design/methodology - This study focuses on tariffs, GDP growth rates, and the proportion of regional FDI to total FDI as research variables, and employs a panel vector autoregression model and GMM-style estimator to investigate the dynamic relationship between RCEP and intra-industry trade among member countries. Findings - The study finds that the level of intra-industry trade between member states is positively impacted by both tariffs and intra-regional FDI. The impulse response graph shows that tariffs and FDI within the region can promote intra-industry trade among member countries, with a quick response. However, the contribution rates of tariffs and intra-regional FDI are not particularly high at approximately 1.5% and 1.4%, respectively. In contrast, the contribution rate of GDP growth can reach around 8.5%. This implies that the influence of economic growth rate on intra-regional trade in industries is not only long-term but also more powerful than that of tariffs and intra-regional FDI. Originality/value - The originality of this study lies in providing a new approach to investigating the potential impact of RCEP while avoiding the limitations associated with the GTAP model. Additionally, this study addresses existing gaps within the research, further contributing to the research merit of the study.

이자율(利子率)의 변화(變化)가 임산물수입(林産物輸入)에 미치는 영향 (Impacts of the Interest Rate Change on the Forest Products Import Quantities in Korea)

  • 김동준
    • 한국산림과학회지
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    • 제90권5호
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    • pp.663-671
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    • 2001
  • 이 연구는 이자율의 변화가 임산물수입량에 미치는 영향을 우리나라 시장을 대상으로 분석하였다. 첫번째 목적은 이자율의 변화가 임산물수입량 변화의 원인이 되는지, 즉 인과관계를 파악하는 것이고, 두번째 목적은 이자율의 변화가 임산물수입량에 얼마만큼 얼마동안 영향을 미치는지, 즉 동태적 영향을 추정하는 것이다. 이자율과 임산물수입량의 관계는 자기회귀모형에 의해 만들어졌다. 인과관계 파악은 인과성검정을 이용하였고, 동태분석은 분산분해분석과 충격반응분석을 이용하였다. 결과에 의하면 이자율의 변화는 임산물 중에서 합판수입량 변화의 원인이 되었다. 합판의 경우에 어느 시기의 수입량은 그 시기 이전의 이자율에 의해 20%, 그 시기 이전의 수입량에 의해 80% 가량 설명되었다. 또한 이자율의 변화는 합판수입량에 6개월까지 영향을 미쳤다. 즉 이자율의 변화가 합판수입량에 영향을 미쳤더라도 단기간에 불과했다.

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VAR와 그래프이론을 이용한 시계열의 인과성 분석 -미국 대두 가격 사례분석- (Time-Series Causality Analysis using VAR and Graph Theory: The Case of U.S. Soybean Markets)

  • 박호정;윤원철
    • 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.

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Asset Price, the Exchange Rate, and Trade Balances in China: A Sign Restriction VAR Approach

  • Kim, Wongi
    • 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%.

자기상관자료를 갖는 공정을 위한 다변량 관리도 (Multivariate Control Chart for Autocorrelated Process)

  • 남국현;장영순;배도선
    • 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.

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EMG Pattern Recognition based on Evidence Accumulation for Prosthesis Control

  • Lee, Seok-Pil;Park, Sand-Hui
    • 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.

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Dynamic bivariate correlation methods comparison study in fMRI

  • Jaehee Kim
    • Communications for Statistical Applications and Methods
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    • 제31권1호
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    • pp.87-104
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    • 2024
  • Most functional magnetic resonance imaging (fMRI) studies in resting state have assumed that the functional connectivity (FC) between time series from distinct brain regions is constant. However, increased interest has recently been in quantifying possible dynamic changes in FC during fMRI experiments. FC study may provide insight into the fundamental workings of brain networks to brain activity. In this work, we focus on the specific problem of estimating the dynamic behavior of pairwise correlations between time courses extracted from two different brain regions. We compare the sliding-window techniques such as moving average (MA) and exponentially weighted moving average (EWMA), dynamic causality with vector autoregressive (VAR) model, dynamic conditional correlation (DCC) based on volatility, and the proposed alternative methods to use differencing and recursive residuals. We investigate the properties of those techniques in a series of simulation studies. We also provide an application with major depressive disorder (MDD) patient fMRI data to demonstrate studying dynamic correlations.

International Transmission of Macroeconomic Uncertainty in China: A Time-varying Bayesian Global SVAR Approach

  • Wongi Kim
    • East Asian Economic Review
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    • 제28권1호
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    • pp.95-140
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    • 2024
  • This study empirically investigates the international transmission of China's uncertainty shocks. It estimates a time-varying parameter Bayesian global structural vector autoregressive model (TVP-BGVAR) using time series data for 33 countries to evaluate heterogeneous international linkage across countries and time. Uncertainty shocks are identified via sign restrictions. The empirical results reveal that an increase in uncertainty in China negatively affects the global economy, but those effects significantly vary over time. The effects of China's uncertainty shocks on the global economy have been significantly altered by China's WTO accession, the global financial crisis, and the recent US-China trade conflict. Furthermore, the effects of China's uncertainty shocks, typically on inflation, differ significantly across countries. Moreover, Trade openness appears crucial in explaining heterogeneous GDP responses across countries, whereas the international dimension of monetary policy appears to be important in explaining heterogeneous inflation responses across countries.

우리나라 소비자물가상승률 예측 (Forecasting Korean CPI Inflation)

  • 강규호;김정성;신세림
    • 경제분석
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    • 제27권4호
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    • pp.1-42
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    • 2021
  • 우리나라 소비자물가상승률에 대한 예측은 한국은행의 물가안정목표제 운용, 채권시장 참가자의 만기 포트폴리오 최적화, 부동산 시장 및 민간의 소비와 투자 등 경제 전반에 지대한 영향을 미친다. 본 연구는 향후 3년간 우리나라 소비자물가상승률 예측결과를 제시한다. 이를 위해 우선 자기회귀시차(Autoregressive Distributed Lag, ADL) 모형, AR 모형, 소규모 벡터자기회귀(VAR) 모형, 대규모 VAR 모형의 표본외 예측력을 기준으로 모형선택을 실시한다. 물가상승률에는 다수의 잠재적인 예측변수가 존재하기 때문에 12개의 거시변수를 대상으로 ADL 모형에 베이지안 변수선택기법을 도입하고, 예측력 향상을 위한 정밀한 튜닝과정을 고안하고 적용하였다. VAR 모형에는 미네소타 사전분포를 설정하여 차원의 저주 문제를 극복하고자 하였다. 최근 5년을 대상으로 한 장단기 표본외 예측결과, ADL 모형이 점예측과 분포예측 모두에서 여타 경쟁모형에 비해 전반적으로 우월하였다. 예측조합을 통한 예측결과, 우리나라 소비자물가상승률이 2022년 하반기까지는 현재 비슷한 2% 내외의 수준을 유지할 것으로 보이며, 2023년 상반기부터는 1% 내외로 하락할 것으로 전망된다. 80% 신용구간은 예측치의 대략 ±1%p이다.