Cointegration

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

Car ownership and use in Britain: a comparison of the empirical results of alternative cointegration estimation methods and forecasts

Abstract: This paper addresses two problems faced by many forecasters in the transport sector, namely how to use a relatively small sample to forecast car ownership over a long period of time and avoid the difficulties caused by spurious or nonsense regressions. Five alternative estimation methods are used to test for cointegrating relationships between per capita car ownership (and use) and real per capita personable disposable income, real motoring costs and real bus fares. These are the Engle-Granger two-stage, the Phillips-Hansen fully modified, the Wickens-Breusch one-stage, the autoregressive distributed lag, and the Johansen maximum likelihood methods. The corresponding error correction models are estimated, and a comparison made between the derived short- and long-run demand elasticities for car ownership and use. The ex-post forecasting performance of the error correction models, together with an ARIMA model specification, is evaluated using a number of performance criteria. The long-range time series fore...

A Survey of Some Recent Econometric Methods

Abstract: This survey is written mainly for the applied economist though we hope that the specialist econometrician will find something of interest too. Our aim has been to try to bring the applied worker as up to date as possible, and in the process to improve the quality of applied work, by providing access to the latest ideas in econometrics. We have tried to describe and explain in a relatively non-technical way the main developments that have taken place in the last ten years. Partly for reasons of space the survey is by no means exhaustive but it does cover a wide range of topics in both time-series and cross-section analysis. Among the subjects covered are the followoing: the nature of data (including integrated and fractionally integrated data), four estimation methods (maximum likelihood, method of moments, M-estimators and non-parametric estimation), inference (with stationary and integrated regressors), a comparison of various model evaluation principles, the formulation of models (including dynamic specification, cointegration and conditional expectations in mean and variance). Copyright 1989 by Royal Economic Society.

The effect of carbon dioxide emission and the consumption of electrical energy, fossil fuel energy, and renewable energy, on economic performance: evidence from Pakistan.

Abstract: Energy affects the economic growth and development of a country. Renewable energy has become an important part of the world’s energy consumption. The use of fossil fuel energy contributes to global warming and carbon dioxide emissions, and has a detrimental effect on the environment. The long-run and short-run causality relationships between electric power consumption, renewable electricity output, renewable energy consumption, fossil fuel energy consumption, energy use, carbon dioxide emissions, and gross domestic product per capita for Pakistan over the period of 1990–2017 were investigated in this paper using the autoregressive distributed lag bounds testing approach to cointegration. The augmented Dickey–Fuller unit root test and the Phillips–Perron unit root test were used to check the stationarity of the variables, while the Johansen cointegration test was applied to check the robustness of the long-run relationships. The Granger causality test under the vector error correction model extracted during the short-run estimation showed a unidirectional relationship among all variables except for the relationship between gross domestic product per capita and carbon dioxide emission, which was bidirectional (feedback hypothesis). The evidence showed that in the long run, carbon dioxide emissions, electric power consumption, and renewable electricity output had a positive and significant relationship with the gross domestic product per capita, while the relationship of renewable energy consumption, energy use, and fossil fuel energy consumption with the gross domestic product per capita had a negative effect. Overall, the long-run effects of the variables were found to have a stronger effect on the gross domestic product per capita than the short-run dynamics, which indicated that the findings were heterogeneous. The evidence suggests that the government of Pakistan should take steps to enhance the use of renewable energy resources to resolve the energy crisis in the country and introduce new policies to reduce carbon dioxide emissions.