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Bayesian nonlinear panel cointegration: an empirical application to the EKC hypothesis

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  • Michael L. Polemis

    (University of Piraeus
    Hellenic Competition Commission)

  • Mike G. Tsionas

    (Lancaster University Management School)

Abstract

In this paper we employ a new Bayesian approach within a cointegrating regression framework along the lines of Breitung (J Bus Econ Stat 19:331–340, 2001). We extend this fully to the case of panel data framework with general cross-sectional dependence. We argue that our Bayesian technique does not depend on asymptotic critical values and in our empirical application we do find strong evidence of nonlinear cointegrated relationships between local (SO2 and NOX) and global (CO2) emissions with the level of economic growth.

Suggested Citation

  • Michael L. Polemis & Mike G. Tsionas, 2019. "Bayesian nonlinear panel cointegration: an empirical application to the EKC hypothesis," Letters in Spatial and Resource Sciences, Springer, vol. 12(2), pages 113-120, August.
    • Handle: RePEc:spr:lsprsc:v:12:y:2019:i:2:d:10.1007_s12076-019-00230-4
    DOI: 10.1007/s12076-019-00230-4
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    References listed on IDEAS

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    Cited by:

    1. Charalampos Agiropoulos & Michael L. Polemis & Michael Siopsis & Sotiris Karkalakos, 2022. "Revisiting the finance‐growth nexus: A socioeconomic approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 2762-2783, July.

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    More about this item

    Keywords

    Bayesian inference; EKC hypothesis; Nonlinear panel cointegration; MCMC; Posterior distribution;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy

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