Cointegration

About: Cointegration is a research topic. Over the lifetime, 17130 publications have been published within this topic receiving 506215 citations.

Estimation of Environmental Kuznets Curve for CO2 Emission: Role of Renewable Energy Generation in India

Abstract: The existing literature on environmental Kuznets curve (EKC) is mainly focused on finding out the optimal sustainable path for any economy. Looking at the present renewable energy generation scenario in India, this study has made an attempt to estimate the EKC for CO2 emission in India for the period of 1971-2015. Using unit root test with multiple structural breaks and autoregressive distributed lag (ARDL) approach to cointegration, this study has found the evidence of inverted U-shaped EKC for India, with the turnaround point at USD 2937.77. The renewable energy has found to have significant negative impact on CO2 emissions, whereas for overall energy consumption, the long run elasticity is found to be higher than short run elasticity. Moreover, trade is negatively linked with carbon emissions. Based on the results, this study concludes with suitable policy prescriptions.

The Internationalisation of Stock Markets and the Abolition of U.K. Exchange Control

Abstract: This paper aims to assess the impact of the abolition of U.K. exchange control on the degre of integration of U.K. and overseas stock markets. Using cointegration techniques, we find that although there is no significant increase in the correlation of short-run stock market returns for the United Kingdom and certain overseas markets post 1979, there does appear to be a marked increase in the degree to which these markets move together in the long run after this date--there appears to be no long-run gain from diversification. Since cointegration of two variables implies that at least one of them can be used to help forecast the other, our findings also imply the inefficiency of a number of stock markets

The Emerging U.S. Current Account Deficit in the 1980s: A Cointegration Analysis.

Abstract: This paper seeks to understand the recent history of U.S. external imbalances by identifying the "long-run tendency" of the U.S. current account balance and investigating its behavior. The procedure that is adopted is to estimate cointegrating regressions between U.S. exports and imports of goods and services. Estimates from cointegrating regressions between several measures of U.S. exports and imports show that up to about the end of 1983 the U.S. current account tended toward zero. Since that time, there has been an apparent structural shift resulting in a long-run tendency for a deficit in excess of $100 billion per year. Copyright 1992 by MIT Press.

Estimation and Inference in Short Panel Vector Autoregressions with Unit Roots and Cointegration

Abstract: This paper considers estimation and inference in panel vector autoregressions (PVARs) with fixed effects when the time dimension of the panel is finite, and the cross-sectional dimension is large. A Maximum Likelihood (ML) estimator based on a transformed likelihood function is proposed and shown to be consistent and asymptotically normally distributed irrespective of the unit root and cointegrating properties of the underlying PVAR model. The transformed likelihood framework is also used to derive unit root and cointegration tests in panels with short time dimension; these tests have the attractive feature that they are based on standard chi-square and normal distributed statistics. Examining Generalized Method of Moments (GMM) estimation as an alternative to our proposed ML estimator, it is shown that conventional GMM estimators based on standard orthogonality conditons break down if the underlying time series contain unit roots. Also, the implementation of extended GMM estimators making use of variants of homoskedasticity and stationarity restrictions as suggested in the literature in a univariate context is subject to difficulties. Monte Carlo evidence is adduced suggesting that the ML estimator and parameter hypothesis and cointegration tests based on it perform well in small sample; this is in marked contrast to the small sample performance of the GMM estimators.