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System Estimation of Panel Data Models Under Long-Range Dependence

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  • Yunus Emre Ergemen

Abstract

A general dynamic panel data model is considered that incorporates individual and interactive fixed effects allowing for contemporaneous correlation in model innovations. The model accommodates general stationary or nonstationary long-range dependence through interactive fixed effects and innovations, removing the necessity to perform a priori unit-root or stationarity testing. Moreover, persistence in innovations and interactive fixed effects allows for cointegration; innovations can also have vector-autoregressive dynamics; deterministic trends can be featured. Estimations are performed using conditional-sum-of-squares criteria based on projected series by which latent characteristics are proxied. Resulting estimates are consistent and asymptotically normal at standard parametric rates. A simulation study provides reliability on the estimation method. The method is then applied to the long-run relationship between debt and GDP. Supplementary materials for this article are available online.

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  • Yunus Emre Ergemen, 2019. "System Estimation of Panel Data Models Under Long-Range Dependence," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(1), pages 13-26, January.
    • Handle: RePEc:taf:jnlbes:v:37:y:2019:i:1:p:13-26
    DOI: 10.1080/07350015.2016.1255217
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    Cited by:

    1. Thomaidis, Nikolaos S. & Biskas, Pandelis N., 2021. "Fundamental pricing laws and long memory effects in the day-ahead power market," Energy Economics, Elsevier, vol. 100(C).
    2. Daniel Borup & Bent Jesper Christensen & Yunus Emre Ergemen, 2019. "Assessing predictive accuracy in panel data models with long-range dependence," CREATES Research Papers 2019-04, Department of Economics and Business Economics, Aarhus University.
    3. Guglielmo Maria Caporale & Luis Alberiko Gil‐Alana, 2022. "Trends and cycles in macro series: The case of US real GDP," Bulletin of Economic Research, Wiley Blackwell, vol. 74(1), pages 123-134, January.
    4. Jorge V Pérez-Rodríguez & Heiko Rachinger & María Santana-Gallego, 2022. "Does tourism promote economic growth? A fractionally integrated heterogeneous panel data analysis," Tourism Economics, , vol. 28(5), pages 1355-1376, August.
    5. Tobias Hartl, 2020. "Macroeconomic Forecasting with Fractional Factor Models," Papers 2005.04897, arXiv.org.
    6. Michail I. Seitaridis & Nikolaos S. Thomaidis & Pandelis N. Biskas, 2021. "Fundamental Responsiveness in European Electricity Prices," Energies, MDPI, vol. 14(22), pages 1-14, November.
    7. Juan L Eugenio-Martin & Roberto Patuelli, 2022. "Panel data models in tourism research: Innovative applications and methods," Tourism Economics, , vol. 28(5), pages 1348-1354, August.

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