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A moving blocks empirical likelihood method for panel linear fixed effects models with serial correlations and cross-sectional dependences

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  • Qiu, Jin
  • Ma, Qing
  • Wu, Lang

Abstract

To simultaneously deal with serial correlations and cross-sectional dependences for a panel linear fixed effects model, we propose a new approach based on an extended score vector and a moving blocks empirical likelihood method. Large sample properties of the proposed method are studied. Simulation results show that the new method works well under the situations of either strong or weak cross-sectional dependences, and the method performs better than the methods in Gonçalves (2011) and Vogelsang (2012). The proposed method is also applied to an application in carbon emission, and the results show that urbanization has a significant effect on carbon emission. Moreover, the effect varies in different stage of urbanization.

Suggested Citation

  • Qiu, Jin & Ma, Qing & Wu, Lang, 2019. "A moving blocks empirical likelihood method for panel linear fixed effects models with serial correlations and cross-sectional dependences," Economic Modelling, Elsevier, vol. 83(C), pages 394-405.
    • Handle: RePEc:eee:ecmode:v:83:y:2019:i:c:p:394-405
    DOI: 10.1016/j.econmod.2019.09.029
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    Cited by:

    1. Mohammad Mafizur Rahman & Khosrul Alam, 2022. "CO 2 Emissions in Asia–Pacific Region: Do Energy Use, Economic Growth, Financial Development, and International Trade Have Detrimental Effects?," Sustainability, MDPI, vol. 14(9), pages 1-16, April.
    2. Wojciech Charemza & Svetlana Makarova & Krzysztof Rybiński, 2023. "Anti-pandemic restrictions, uncertainty and sentiment in seven countries," Economic Change and Restructuring, Springer, vol. 56(1), pages 1-27, February.
    3. Rahman, Mohammad Mafizur & Alam, Khosrul, 2022. "Effects of corruption, technological innovation, globalisation, and renewable energy on carbon emissions in Asian countries," Utilities Policy, Elsevier, vol. 79(C).

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