• Journal of the Korean Data and Information Science Society
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• v.18 no.1
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• pp.73-80
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• 2007

Cointegration(together with VARMA(vector ARMA)) has been proven to be useful for analyzing multivariate non-stationary data in the field of financial time series. It provides a linear combination (which turns out to be stationary series) of non-stationary component series. This linear combination equation is referred to as long term equilibrium between the component series. We consider two sets of Korean bivariate financial time series and then illustrate cointegration analysis. Specifically estimated VAR(vector AR) and VECM(vector error correction model) are obtained and CV(cointegrating vector) is found for each data sets.

• Journal of Korea Society of Industrial Information Systems
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• v.18 no.4
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• pp.61-72
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• 2013

The relationship between export growth and economic growth in developing countries has been one of the main issues in the growth theory field. Many of empirical studies have been done during the last three decades in order to investigate the export-led growth hypothesis using either time-series or cross-sectional data mainly in developing countries. This paper applies cointegration and error correction models to test causal relationship between export growth and economic growth in the Korean 8 manufacturing industries using the industrial time-series quarterly data over 1975-2010. The export-output relationship is tested by including industrial capital stock and the industrial labor force as exogenous variables. The cointegration and error-correction modelling technique with industrial export and output data have showed the strong evidence that there is a bi-directional causality between industrial export and industrial output in 6 manufacturing industries except wood & pulp and nonmetallic industries.

• Environmental and Resource Economics Review
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• v.9 no.2
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• pp.373-391
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• 2000

This paper investigates the existence of a long-run relationship between world oil price and consumer price index for Korea during 1983~1999. The cointegration and error correction modelling approaches have been applied. Empirical results suggest that there exists a long-run relationship among world oil prices. consumer prices, M2 and a production gap variable. The dynamic behavior of the relationship has been investigated by estimating a error correction model, in which the error correction term have been found significant. The error correction model has also been found to be robust as it satisfy almost all relevant diagnostic tests.

• Journal of the Korean Data and Information Science Society
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• v.16 no.3
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• pp.695-703
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• 2005

This paper extends the maximum likelihood seasonal cointegration procedure developed by Johansen and Schaumburg (1999) for daily time series. The finite sample distribution of the associated rank test for dally data is also presented.

• Journal of the Korean Data and Information Science Society
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• v.22 no.3
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• pp.401-412
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• 2011

This study uses a vector error correction model to analyze the daily time series data of the spot price of EUA (European Union Allowance). As endogenous variables, five variables are considered for the analysis, including prices of crude oil, natural gas, electricity and coal in addition to carbon price. Data period is Phase 2 period (April 21, 2008 to March 31, 2010) to avoid Phase 1 period (2005-2007) where the EUA prices were distorted. Unit-root and cointegration test results reveal that all variables have a unit root and cointegration vectors exist, so a vector error correction model is adopted instead of a vector autoregressive model.

• Proceedings of the Korean Statistical Society Conference
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• 2005.05a
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• pp.13-15
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• 2005

In this paper, we propose an extension of the maximum likelihood seasonal cointegration procedure developed by Johansen and Schaumburg (1999) for daily time series. We presented the finite sample distribution of the associated rank test statistics for daily data.

• Journal of the Korean Data and Information Science Society
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• v.27 no.6
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• pp.1573-1583
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• 2016

It is necessary to check mutual correlations between related variables because housing prices are influenced by a lot of variables of the economy both internally and externally. In this paper, employing the Granger causality test, we have validated interrelated relationship between the variables. In addition, there is cointegration associations in the results of the cointegration test between the variables. Therefore, an analysis using a vector error correction model including an error correction term has been attempted. As a result of the empirical comparative analysis of the forecasting performance with ARIMA and VAR models, it is confirmed that the forecasting performance by vector error correction model is superior to those of the former two models.

• Korean Journal of Agricultural Science
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• v.47 no.3
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• pp.567-576
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• 2020

One of the main challenges receiving much attention in the Rwandan agriculture and food industry in recent decades is the increases in maize prices. Indeed, a rise in maize prices causes higher living expenses for households because maize, which is a major staple food crop, constitutes a significant share of total food consumption among households in Rwanda. The aim of this study was to assess the extent of integration and how prices are transmitted between retail and wholesale markets of domestic maize in Rwanda. This study used monthly data of retail and wholesale prices of maize from January 1995 to December 2019. This empirical investigation was based on a linear cointegration approach and an asymmetric error correction model framework. Using the augmented dickey-fuller residual-based test and the Johansen Maximum Likelihood cointegration test, the results revealed that the retail and wholesale markets of maize are integrated. Hence, prices in these markets do not drift apart in the long run. The results of the Granger causality test revealed that there is a unidirectional causal relationship flowing from wholesale prices to retail prices, i.e., wholesale prices influence retail prices. Accordingly, the results from the asymmetric error correction model confirmed the presence of a positive asymmetric price transmission between wholesale and retail prices of maize in Rwanda. Thus, we suggest that policymakers take a critical look at the causes and factors that may influence asymmetry price transmission.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.1
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• pp.37-46
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• 2020

The paper examines the dynamic relationship of domestic credit and stock market liquidity on the economic growth of the Philippines from 1995 to 2018 applying the autoregressive distributed lag (ARDL) bounds testing approach to cointegration, together with Granger causality test based on vector error correction model (VECM). The ARDL model indicated a long-run relationship of domestic credit and stock market liquidity on GDP growth. When the GDP per capita is the dependent variable there is weak cointegration. Also, the Johansen cointegration test confirmed the existence of long-run relationship of domestic credit and stock market liquidity both on GDP growth and GDP per capita. The VECM concludes a long-run causality running from domestic credit and stock market liquidity to GDP growth. At levels, domestic credit has significant short-run causal relationship with GDP growth. As for stock market liquidity at first lag, has significant short-run causal relationship with GDP growth. With regards to VECM for GDP per capita, domestic credit and stock market liquidity indicates no significant dynamic adjustment to a new equilibrium if a disturbance occurs in the whole system. At levels, the results indicated the presence of short-run causality from stock market liquidity and GDP per capita. The CUSUMSQ plot complements the findings of the CUSUM plot that the estimated models for GDP growth and GDP per capita were stable.

• Communications for Statistical Applications and Methods
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• v.15 no.3
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• pp.411-420
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• 2008

This paper considers a feasible two-step estimator for seasonal cointegration as the extension of $Br{\ddot{u}}ggeman$ and $L{\ddot{u}}tkepohl$ (2005). It is shown that the reducedrank maximum likelihood(ML) estimator for seasonal cointegration can still produce occasional outliers as that for non-seasonal cointegration even though the sizes of them are not extreme as those in non-seasonal cointegration. The ML estimator(MLE) is compared with the two-step estimator in a small Monte Carlo simulation study and we find that the two-step estimator can be an attractive alternative to the MLE, especially, in a small sample.