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Energy production in Brazil: Empirical facts based on persistence, seasonality and breaks

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  • Barros, Carlos P.
  • Gil-Alana, Luis A.
  • Wanke, Peter

Abstract

This paper investigates the statistical properties of the production of energy in Brazil using long range dependence techniques in monthly data from January 2000 to February 2013. Two important features of the data are analyzed, in particular, its degree of persistence and seasonality. The results indicate first that seasonality is an essential issue in modeling the persistence in energy production. Also, the persistence itself, measured in terms of the differencing parameter is relevant, with orders of integration in the series found to be positive though smaller than 1 and thus implying mean reversion. A single structural break is also found in two of the series. Policy implications of the results obtained are also derived.

Suggested Citation

  • Barros, Carlos P. & Gil-Alana, Luis A. & Wanke, Peter, 2016. "Energy production in Brazil: Empirical facts based on persistence, seasonality and breaks," Energy Economics, Elsevier, vol. 54(C), pages 88-95.
  • Handle: RePEc:eee:eneeco:v:54:y:2016:i:c:p:88-95
    DOI: 10.1016/j.eneco.2015.11.002
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    References listed on IDEAS

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    Cited by:

    1. Luis Alberiko Gil-Alaña & Carlos Pestana Barros & Zhongfei Chen, 2016. "The persistence of air pollution in four mega-cities of China," NCID Working Papers 04/2016, Navarra Center for International Development, University of Navarra.
    2. Mishra, Vinod & Smyth, Russell, 2017. "Conditional convergence in Australia's energy consumption at the sector level," Energy Economics, Elsevier, vol. 62(C), pages 396-403.
    3. repec:eee:rensus:v:90:y:2018:i:c:p:370-378 is not listed on IDEAS

    More about this item

    Keywords

    Brazil; Energy production; Long memory;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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