Topic
Cointegration
About: Cointegration is a research topic. Over the lifetime, 17130 publications have been published within this topic receiving 506215 citations.
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TL;DR: The authors investigated the causal relationship between immigration and per capita economic growth and found evidence of long-run causality from economic growth to immigration, but not vice versa, using the ARDL approach to cointegration.
201 citations
TL;DR: In this article, the relationship between gasoline demand, national income and price of gasoline is empirically examined using cointegration and error correction techniques, and it has been found that gasoline demand is likely to increase significantly for a given increase in the gross domestic product.
201 citations
TL;DR: In this paper, the authors used the autoregressive distributed lag (ARDL) model to estimate a demand relationship for Danish residential energy consumption, and the ARDL estimates were compared to the estimates obtained using cointegration techniques and error-correction models (ECM's).
200 citations
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TL;DR: In this paper, the authors attempted to analyze the dynamics of renewable energy consumption, economic growth, and CO2 emissions using structural VAR approach using unit root tests and showed that all variables are non-stationary at their level form and stationary in first difference form and cointegration analysis.
Abstract: This study has attempted to analyze the dynamics of renewable energy consumption, economic growth, and CO2 emissions For the analysis, we used structural VAR approach Results of unit root tests show that all variables are non-stationary at their level form and stationary in first difference form and cointegration analysis, analyzed through Johansen-Juselius (1990), shows that there is no evidence of cointegration among the test variables The innovations analysis of study reveals that a positive shock on the consumption of renewable energy source increases GDP and decreases CO2 emissions and a positive shock on GDP have a very high positive impact on the CO2 emissions The variance decomposition shows the share of consumption of renewable energy source explained a significant part of the forecast error variance of GDP and a relatively smaller or negligible part of the forecast error variance of CO2 emissions
200 citations
TL;DR: In this paper, structural time series models by which a time series can be decomposed as the sum of a trend, seasonal and irregular components are presented, and the recursive estimation and smoothing by means of the Kalman lter algorithm is described taking into account its different stages, from initialisation to parameter's estimation.
Abstract: The continued increase in availability of economic data in recent years and, more importantly, the possibility to construct larger frequency time series, have fostered the use (and development) of statistical and econometric techniques to treat them more accurately. This paper presents an exposition of structural time series models by which a time series can be decomposed as the sum of a trend, seasonal and irregular components. In addition to a detailled analysis of univariate speci cations we also address the SUTSE multivariate case and the issue of cointegration. Finally, the recursive estimation and smoothing by means of the Kalman lter algorithm is described taking into account its different stages, from initialisation to parameter's estimation.
199 citations