• Title, Summary, Keyword: 벡터자기회귀

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Adaptive lasso in sparse vector autoregressive models (Adaptive lasso를 이용한 희박벡터자기회귀모형에서의 변수 선택)

  • Lee, Sl Gi;Baek, Changryong
    • The Korean Journal of Applied Statistics
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    • v.29 no.1
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    • pp.27-39
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    • 2016
  • This paper considers variable selection in the sparse vector autoregressive (sVAR) model where sparsity comes from setting small coefficients to exact zeros. In the estimation perspective, Davis et al. (2015) showed that the lasso type of regularization method is successful because it provides a simultaneous variable selection and parameter estimation even for time series data. However, their simulations study reports that the regular lasso overestimates the number of non-zero coefficients, hence its finite sample performance needs improvements. In this article, we show that the adaptive lasso significantly improves the performance where the adaptive lasso finds the sparsity patterns superior to the regular lasso. Some tuning parameter selections in the adaptive lasso are also discussed from the simulations study.

Estimation of nonlinear GARCH-M model (비선형 평균 일반화 이분산 자기회귀모형의 추정)

  • Shim, Joo-Yong;Lee, Jang-Taek
    • Journal of the Korean Data and Information Science Society
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    • v.21 no.5
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    • pp.831-839
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    • 2010
  • Least squares support vector machine (LS-SVM) is a kernel trick gaining a lot of popularities in the regression and classification problems. We use LS-SVM to propose a iterative algorithm for a nonlinear generalized autoregressive conditional heteroscedasticity model in the mean (GARCH-M) model to estimate the mean and the conditional volatility of stock market returns. The proposed method combines a weighted LS-SVM for the mean and unweighted LS-SVM for the conditional volatility. In this paper, we show that nonlinear GARCH-M models have a higher performance than the linear GARCH model and the linear GARCH-M model via real data estimations.

Filtered Coupling Measures for Variable Selection in Sparse Vector Autoregressive Modeling (필터링된 잔차를 이용한 희박벡터자기회귀모형에서의 변수 선택 측도)

  • Lee, Seungkyu;Baek, Changryong
    • The Korean Journal of Applied Statistics
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    • v.28 no.5
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    • pp.871-883
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    • 2015
  • Vector autoregressive (VAR) models in high dimension suffer from noisy estimates, unstable predictions and hard interpretation. Consequently, the sparse vector autoregressive (sVAR) model, which forces many small coefficients in VAR to exactly zero, has been suggested and proven effective for the modeling of high dimensional time series data. This paper studies coupling measures to select non-zero coefficients in sVAR. The basic idea based on the simulation study reveals that removing the effect of other variables greatly improves the performance of coupling measures. sVAR model coefficients are asymmetric; therefore, asymmetric coupling measures such as Granger causality improve computational costs. We propose two asymmetric coupling measures, filtered-cross-correlation and filtered-Granger-causality, based on the filtered residuals series. Our proposed coupling measures are proven adequate for heavy-tailed and high order sVAR models in the simulation study.

Causal Analysis between the Korean and the U.S. Monthly Business Conditions (한미 월간 경기동향의 선행성 분석)

  • Kim, Tae-Ho
    • The Korean Journal of Applied Statistics
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    • v.22 no.1
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    • pp.17-28
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    • 2009
  • This study attempts to perform the statistical test for the causality between the Korean and the U.S. business conditions in association with the lead-lag relationship between the domestic stock price and the business condition. Their causal relationships are clearly identified after the outbreak of the IMF financial crisis. The vector autoregression for the corresponding period appears to reflect the strong interrelationships between the market variables and the dependency of the domestic business conditions on the U.S. market. The estimation results validate the leading effect of the stock price and the U.S. business behavior.

Comparison of Forecasting Performance in Multivariate Nonstationary Seasonal Time Series Models (다변량 비정상 계절형 시계열모형의 예측력 비교)

  • Seong, Byeong-Chan
    • Communications for Statistical Applications and Methods
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    • v.18 no.1
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    • pp.13-21
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    • 2011
  • This paper studies the analysis of multivariate nonstationary time series with seasonality. Three types of multivariate time series models are considered: seasonal cointegration model, nonseasonal cointegration model with seasonal dummies, and vector autoregressive model in seasonal differences that are compared for forecasting performances using Korean macro-economic time series data. The cointegration models produce smaller forecast errors in short horizons; however, when longer forecasting periods are considered the vector autoregressive model appears preferable.

Wild bootstrap Ljung-Box test for autocorrelation in vector autoregressive and error correction models (벡터자기회귀모형과 오차수정모형의 자기상관성을 위한 와일드 붓스트랩 Ljung-Box 검정)

  • Lee, Myeongwoo;Lee, Taewook
    • The Korean Journal of Applied Statistics
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    • v.29 no.1
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    • pp.61-73
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    • 2016
  • We consider the wild bootstrap Ljung-Box (LB) test for autocorrelation in residuals of fitted multivariate time series models. The asymptotic chi-square distribution under the IID assumption is traditionally used for the LB test; however, size distortion tends to occur in the usage of the LB test, due to the conditional heteroskedasticity of financial time series. In order to overcome such defects, we propose the wild bootstrap LB test for autocorrelation in residuals of fitted vector autoregressive and error correction models. The simulation study and real data analysis are conducted for finite sample performance.

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

  • Park, Hojeong;Yun, Won-Cheol
    • Environmental and Resource Economics Review
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    • v.12 no.4
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    • pp.687-708
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    • 2003
  • The purpose of this paper is to introduce time-series causality analysis by combining time-series technique with graph theory. Vector autoregressive (VAR) models can provide reasonable interpretation only when the contemporaneous variables stand in a well-defined causal order. We show that how graph theory can be applied to search for the causal structure In VAR analysis. Using Maryland crop cash prices and CBOT futures price data, we estimate a VAR model with directed acyclic graph analysis. This expands our understanding the degree of interconnectivity between the employed time-series variables.

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Estimating GARCH models using kernel machine learning (커널기계 기법을 이용한 일반화 이분산자기회귀모형 추정)

  • Hwang, Chang-Ha;Shin, Sa-Im
    • Journal of the Korean Data and Information Science Society
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    • v.21 no.3
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    • pp.419-425
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    • 2010
  • Kernel machine learning is gaining a lot of popularities in analyzing large or high dimensional nonlinear data. We use this technique to estimate a GARCH model for predicting the conditional volatility of stock market returns. GARCH models are usually estimated using maximum likelihood (ML) procedures, assuming that the data are normally distributed. In this paper, we show that GARCH models can be estimated using kernel machine learning and that kernel machine has a higher predicting ability than ML methods and support vector machine, when estimating volatility of financial time series data with fat tail.

The sparse vector autoregressive model for PM10 in Korea (희박 벡터자기상관회귀 모형을 이용한 한국의 미세먼지 분석)

  • Lee, Wonseok;Baek, Changryong
    • Journal of the Korean Data and Information Science Society
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    • v.25 no.4
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    • pp.807-817
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    • 2014
  • This paper considers multivariate time series modelling of PM10 data in Korea collected from 2008 to 2011. We consider both temporal and spatial dependencies of PM10 by applying the sparse vector autoregressive (sVAR) modelling proposed by Davis et al. (2013). It utilizes the partial spectral coherence to measure cross correlation between different regions, in turn provides the sparsity in the model while balancing the parsimony of model and the goodness of fit. It is also shown that sVAR performs better than usual vector autoregressive model (VAR) in forecasting.

Autocovariance based estimation in the linear regression model (선형회귀 모형에서 자기공분산 기반 추정)

  • Park, Cheol-Yong
    • Journal of the Korean Data and Information Science Society
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    • v.22 no.5
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    • pp.839-847
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    • 2011
  • In this study, we derive an estimator based on autocovariance for the regression coefficients vector in the multiple linear regression model. This method is suggested by Park (2009), and although this method does not seem to be intuitively attractive, this estimator is unbiased for the regression coefficients vector. When the vectors of exploratory variables satisfy some regularity conditions, under mild conditions which are satisfied when errors are from autoregressive and moving average models, this estimator has asymptotically the same distribution as the least squares estimator and also converges in probability to the regression coefficients vector. Finally we provide a simulation study that the forementioned theoretical results hold for small sample cases.