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Energy Consumption and Economic Growth: Evidence from Nonlinear Panel Cointegration and Causality Tests

Author

Listed:
  • Tolga Omay

    (Cankaya University, Department of International Trade Management)

  • Mubariz Hasanov

    (Hacettepe University, Department of Economics)

  • Nuri Uçar

    (Hacettepe University, Department of Economics)

Abstract
In this paper, we propose a nonlinear cointegration test for heterogeneous panels where the alternative hypothesis is an exponential smooth transition (ESTAR) model. We apply our tests for investigating cointegration relationship between energy consumption and economic growth for the G7 countries covering the period 1977-2007. Moreover, we estimate a nonlinear Panel Vector Error Correction Model in order to analyze the direction of the causality between energy consumption and economic growth. By using nonlinear causality tests we analyze the causality relationships in low economic growth and high economic growth regimes. Furthermore, we deal with the cross section dependency problem in both nonlinear panel cointegration test and nonlinear Panel Vector Error Correction Model.

Suggested Citation

  • Tolga Omay & Mubariz Hasanov & Nuri Uçar, 2012. "Energy Consumption and Economic Growth: Evidence from Nonlinear Panel Cointegration and Causality Tests," Hacettepe University Department of Economics Working Papers 20130, Hacettepe University, Department of Economics.
    • Handle: RePEc:hac:hacwop:20130
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    File URL: http://www.iktisat.hacettepe.edu.tr/WP/WP-2012-10.pdf
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    More about this item

    Keywords

    Nonlinear panel cointegration; nonlinear Panel Vector Error Correction Model; cross section dependency;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • 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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