• Title, Summary, Keyword: Vector Autoregressive Model

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Development of the Lumber Demand Prediction Model

  • Kim, Dong-Jun
    • Journal of Korean Society of Forest Science
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    • v.95 no.5
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    • pp.601-604
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    • 2006
  • This study compared the accuracy of partial multivariate and vector autoregressive models for lumber demand prediction in Korea. The partial multivariate model has three explanatory variables; own price, construction permit area and dummy. The dummy variable reflected the boom of lumber demand in 1988, and the abrupt decrease in 1998. The VAR model consists of two endogenous variables, lumber demand and construction permit area with one lag. On the other hand, the prediction accuracy was estimated by Root Mean Squared Error. The results showed that the estimation by partial multivariate and vector autoregressive model showed similar explanatory power, and the prediction accuracy was similar in the case of using partial multivariate and vector autoregressive model.

Development of the Plywood Demand Prediction Model

  • Kim, Dong-Jun
    • Journal of Korean Society of Forest Science
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    • v.97 no.2
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    • pp.140-143
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    • 2008
  • This study compared the plywood demand prediction accuracy of econometric and vector autoregressive models using Korean data. The econometric model of plywood demand was specified with three explanatory variables; own price, construction permit area, dummy. The vector autoregressive model was specified with lagged endogenous variable, own price, construction permit area and dummy. The dummy variable reflected the abrupt decrease in plywood consumption in the late 1990's. The prediction accuracy was estimated on the basis of Residual Mean Squared Error, Mean Absolute Percentage Error and Theil's Inequality Coefficient. The results showed that the plywood demand prediction can be performed more accurately by econometric model than by vector autoregressive model.

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.

A Causality Analysis of the Hairtail Price by Distribution Channel Using a Vector Autoregressive Model (VAR 모형을 이용한 유통단계별 갈치가격의 인과성 분석)

  • Kim, Cheol-Hyun;Nam, Jong-Oh
    • The Journal of Fisheries Business Administration
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    • v.46 no.1
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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.

Substitution elasticities of the imported and domestically produced pulp and paper (수입펄프.종이와 국산펄프.종이의 대체탄력성)

  • Kim, Se-Bin;Kim, Dong-Jun
    • Korean Journal of Agricultural Science
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    • v.38 no.2
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    • pp.383-391
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    • 2011
  • Traditional international trade theory assumes that import goods and domestically produced goods of the same industry are equal in quality. However the substitutability of the two goods is imperfect. This article estimated the import functions of pulp and paper using econometric and vector autoregressive models, and calculated the elasticities of substitution between imported and domestically produced pulp and paper. The import of pulp is inelastic to import price and domestic price, and elastic to national income in econometric model. And it is inelastic to import price, domestic price and national income in vector autoregressive model. On the other hand, the import of paper is inelastic to domestic price, and elastic to import price and national income in econometric model. And it is inelastic to import price and domestic price, and elastic to national income in vector autoregressive model. The elasticity of substitution between imported and domestically produced pulp was positive, and the elasticity was respectively 0.42 and 0.20 in econometric and vector autoregressive models. This may be because of the high proportion of imports. On the other hand, the elasticity of substitution between imported and domestically produced paper was positive, and the elasticity was respectively 0.75 and 0.81 in econometric and vector autoregressive models. This may be because the quality of imported paper is different from that of domestically produced paper.

Prediction of the interest spread using VAR model (벡터자기회귀모형에 의한 금리스프레드의 예측)

  • Kim, Junhong;Jin, Dalae;Lee, Jisun;Kim, Suji;Son, Young Sook
    • Journal of the Korean Data and Information Science Society
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    • v.23 no.6
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    • pp.1093-1102
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    • 2012
  • In this paper, we predicted the interest spread using the VAR (vector autoregressive) model. Variables used in the VAR model were selected among 56 domestic and foreign macroeconomic time series through crosscorrelation and Granger causality test. The performance of the VAR model was compared with the univariate time series model, AR (autoregressive) model, in view of MAPE (mean absolute percentage error) and RMSE (root mean square error) of forecasts for the last twelve months.

An Analysis of Dynamic Cutting Force Model for Face Milling Using Modified Autoregressive Vector Model (자기회귀 벡터모델을 이용한 정면밀링의 동절삭력 모델해석)

  • 백대균;김정현;김희술
    • Transactions of the Korean Society of Mechanical Engineers
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    • v.17 no.12
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    • pp.2949-2961
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    • 1993
  • Dynamic cutting process can be represented by a closed-loop0 system consisted of machine tool structure and pure cutting process. On this paper, cutting system is modeled as a six degrees of freedom system using MARV(Modified Autoregressive Vector) model in face milling, and the modeled dynamic cutting process is used to predict dynamic cutting force component. Based on the double modulation principle, a dynamic cutting force model is developed. From the simulated relative displacements between tool and workpiece the dynamic force domponents can be calculated, and the dynamic force can be obtained by superposition of the static force and dynamic force components. The simulated dynamic cutting forces have a good agreement with the measured cutting force.

Forecasting volatility via conditional autoregressive value at risk model based on support vector quantile regression

  • Shim, Joo-Yong;Hwang, Chang-Ha
    • Journal of the Korean Data and Information Science Society
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    • v.22 no.3
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    • pp.589-596
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    • 2011
  • The conditional autoregressive value at risk (CAViaR) model is useful for risk management, which does not require the assumption that the conditional distribution does not vary over time but the volatility does. But it does not provide volatility forecasts, which are needed for several important applications such as option pricing and portfolio management. For a variety of probability distributions, it is known that there is a constant relationship between the standard deviation and the distance between symmetric quantiles in the tails of the distribution. This inspires us to use a support vector quantile regression (SVQR) for volatility forecasts with the distance between CAViaR forecasts of symmetric quantiles. Simulated example and real example are provided to indicate the usefulness of proposed forecasting method for volatility.

Analysis of the relationship between garlic and onion acreage response

  • Lee, Eulkyeong;Hong, Seungjee
    • Korean Journal of Agricultural Science
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    • v.43 no.1
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    • pp.136-143
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    • 2016
  • Garlic and onion are staple agricultural products to Koreans and also are important with regard to agricultural producers' income. These products' acreage responses are highly correlated with each other. Therefore, it is necessary to test whether there is a cointegration relationship between garlic acreage and onion acreage when one tries to estimate the acreage response's function. Based upon the test result of cointegration, it is confirmed that there is no statistically significant cointegration relationship between garlic acreage and onion acreage. In this case, vector autoregressive model is preferred to vector error correction model. This study investigated the dynamic relationship between garlic and onion acreage responses using vector autoregressive (VAR) model. The estimated results of VAR acreage response models show that there is a statistically significant relationship between current and lagged acreage of more than one lag. Therefore, it is recommended that government should consider the long-run period's relationship of each product's acreage when it plans a policy for stabilizing the supply and demand of garlic and onion. For the price variables, garlic price only affects garlic acreage response while onion price affects not only onion acreage response but also garlic acreage response. This implies that the stabilizing policy for onion price could have bigger effects than that for garlic price stabilization.

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.