Abstract
This study examines the degree of time persistence in U.S. nuclear electricity net generation using innovative fractional integration and autoregressive models with monthly data from 1973:1 to 2011:10. The results indicate that nuclear electricity net generation is better explained in terms of a long memory model that incorporates persistence components and seasonality. The degree of integration is above 0.5 but significantly below 1.0, suggesting nonstationarity with mean reverting behavior. Policy implications are discussed as well.
Notes
Indeed, nuclear electricity generation has increased as a result of higher utilization of existing capacity and technological advances that have enhanced nuclear plant capacity. With the passage of the Energy Policy Act of 2005 with such incentives as production tax credits for new nuclear power plants, the U.S. Energy Information Administration expects nuclear power output to grow, although at a slower rate than total electricity generation.
The model of [11] is a non-parametric approach of modeling the error term that produces autocorrelations decaying exponentially as in the AR case.
Diagnostic tests conducted on the residuals indicate that this may be the correct specification with no additional autocorrelation in the residuals of the estimated model.
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Acknowledgments
Luis A. Gil-Alana gratefully acknowledges financial support from the Ministry of Economy and Competitiveness (ECO2011-2014 ECON Y FINANZAS, Spain) and from a Jeronimo de Ayanz project of the Government of Navarra. Comments from the Editor and two reviewers are gratefully acknowledged.
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Barros, C.P., Gil-Alana, L.A. & Payne, J.E. Evidence of long memory behavior in U.S. nuclear electricity net generation. Energy Syst 4, 99–107 (2013). https://doi.org/10.1007/s12667-012-0072-y
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DOI: https://doi.org/10.1007/s12667-012-0072-y