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Rice market integration in India
Published in Stephan Pfaffenzeller, Global Commodity Markets and Development Economics, 2018
Madhusudan Ghosh, Atanu Ghoshray
The concept of cointegration is related to the definition of a long-run equilibrium and can serve as a statistical description of some long-run equilibrium relationship posited by economic theory. The fact that two series are cointegrated implies that the integrated series move together in the long run. The variables might diverge in the short-run but the deviations cannot grow over time. As was discussed in the earlier sections, prices in different markets have co-movement if the markets are integrated. Hence, testing for cointegration can be seen to be equivalent to detecting long-run market integration.
Twitter sentiment analysis for price and transaction volume changes in the cryptocurrency market
Published in Siska Noviaristanti, Contemporary Research on Management and Business, 2023
Three methods were employed for further analysis. First, the Augmented Dickey-Fuller (ADF) test was used to determine the stationarity of the time-series data used in this research. (Dickey & Fuller 1979). Second, the Johansen Cointegration test was employed to determine if the time-series used in this research were cointegrated, showing whether there exists a long-term connection between them (Johansen 1988). Lastly, the Granger causality was utilized to see a two-way relationship between two variables, showing whether a certain factor was capable of predicting another factor, or vice versa (Engle & Granger 1987).
Long-term air travel demand forecasting
Published in Yafei Zheng, Kin Keung Lai, Shouyang Wang, Forecasting Air Travel Demand, 2018
Yafei Zheng, Kin Keung Lai, Shouyang Wang
As Figure 8.1 shows, there are three core function modules in this forecasting framework, described as follows: Cointegration relationshipThe ‘Cointegration relationship’ module is mainly aiming at seeking and quantifying the cointegration relationship between the air travel demand and its key explanatory variables. It is usually intuitive to associate the concept of cointegration when doing long-term demand forecasting because cointegration can represent the long-run equilibrium relationship to some extent.
Economic growth and sectoral level electricity consumption nexus in India: new evidence from combined cointegration and frequency domain causality approaches
Published in International Journal of Sustainable Energy, 2022
Mohammed Shameem P, Muhammed Ashiq Villanthenkodath, Krishna Reddy Chittedi
The individual cointegration tests such as Engle and Granger (EG), Johansen (JOH), Peter Boswijk (BO), and Banerje, Dolado, and Mestre (BDM) tests may offer different conclusions while estimating the cointegration of two or more series. Hence, the derived conclusion may not be valid since these tests have lower power. To overcome this, the study uses the Bayer-Hanck test of combined cointegrations test, which combines the aforementioned individual cointegrations tests and provides a joint test statistic with the null hypothesis of the absence of cointegration. The Bayer and Hanck (2013) combined cointegration approach relies on the formula developed by Fisher (1992) to get the level of statistical significance by employing the following Equations (2) and (3).
Does energy innovation play a role in achieving sustainable development goals in BRICS countries?
Published in Environmental Technology, 2022
Muhammad Awais Baloch, Yiting Qiu
A distinct advantage of finding a cointegration relationship among variables is that it confirms the statistical and economic significance of coefficients of independent and dependent variables. However, choosing the right long-run estimator from several estimators is still a question. Supported with recent studies, we select the dynamic ordinary least squares (DOLS) and fully modified ordinary least squares (FMOLS) econometric tools to analyse the long-run association among GHG, GDP, (GDP2), FDI, exports, population, remittances and energy innovation. The DOLS and FMOLS approaches are suitable for present study because these econometric techniques counter the issues of endogeneity and serial correlations in the error terms. The FMOLS is a non-parametric approach therefore it is robust against endogeneity and autocorrelation. Whereas, DOLS method eliminates the problems through lags and leads of explanatory variables by employing parametric techniques. In addition, the DOLS approach provides reliable estimates and more efficient for small sample size. Most importantly, the DOLS method is capable of handling cross-sectional dependence (CSD) and heterogeneity in the data. Therefore, in order to obtain unbiased and reliable estimates, this study employs DOLS and FMOLS methods to analyse the longer run estimates [45,46]. The FMOLS and DOLS models can be expressed as:
Causal Relationship between Nuclear Energy Consumption and Economic Growth: Case of Spain
Published in Strategic Planning for Energy and the Environment, 2018
Korhan Gokmenoglu, Mohamad Kaakeh
Cointegration and error-correction model. If each variable has a unit root, a cointegration test should be conducted to investigate the existence of a long-term relationship between these variables. Because both of our series are nonstationary, the Johansen cointegration test, which uses the maximum likelihood approach, can be used to investigate the long-term relationships among series [42]. The Johansen cointegration test is based on the error correction representation of the vector autoregressive model and its examination.