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Estimation of fractionally integrated panels with fixed effects and cross-section dependence

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  • Ergemen, Yunus Emre
  • Velasco, Carlos

Abstract

We consider a large N,T heterogeneous panel data model with fixed effects, common factors allowing for cross-section dependence, and persistent data and errors, which are assumed fractionally integrated. We propose individual and common-correlation estimates for the slope parameters while error memory parameters are estimated from regression residuals. The individual parameter estimates are all T consistent, asymptotically normal and mutually uncorrelated, irrespective of cointegration between defactored observables. A study of small-sample performance and an empirical application to realized volatility persistence are included.

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  • Ergemen, Yunus Emre & Velasco, Carlos, 2017. "Estimation of fractionally integrated panels with fixed effects and cross-section dependence," Journal of Econometrics, Elsevier, vol. 196(2), pages 248-258.
    • Handle: RePEc:eee:econom:v:196:y:2017:i:2:p:248-258
    DOI: 10.1016/j.jeconom.2016.05.020
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    Cited by:

    1. Yunus Emre Ergemen & Carlos Velasco, 2019. "Persistence Heterogeneity Testing in Panels with Interactive Fixed Effects," Journal of Time Series Analysis, Wiley Blackwell, vol. 40(4), pages 573-589, July.
    2. Eleni Kilipiri & Eugenia Papaioannou & Iordanis Kotzaivazoglou, 2023. "Social Media and Influencer Marketing for Promoting Sustainable Tourism Destinations: The Instagram Case," Sustainability, MDPI, vol. 15(8), pages 1-18, April.
    3. Carlos Vladimir Rodríguez-Caballero & J. Eduardo Vera-Valdés, 2020. "Air pollution and mobility in the Mexico City Metropolitan Area, what drives the COVID-19 death toll?," CREATES Research Papers 2020-15, Department of Economics and Business Economics, Aarhus University.
    4. Ke, Shuyao & Phillips, Peter C.B. & Su, Liangjun, 2024. "Robust inference of panel data models with interactive fixed effects under long memory: A frequency domain approach," Journal of Econometrics, Elsevier, vol. 241(2).
    5. Tobias Hartl & Roland Weigand, 2018. "Multivariate Fractional Components Analysis," Papers 1812.09149, arXiv.org, revised Jan 2019.
    6. 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).
    7. Ergemen, Yunus Emre & Rodríguez-Caballero, C. Vladimir, 2023. "Estimation of a dynamic multi-level factor model with possible long-range dependence," International Journal of Forecasting, Elsevier, vol. 39(1), pages 405-430.
    8. Carlos Vladimir Rodríguez-Caballero, 2016. "Panel Data with Cross-Sectional Dependence Characterized by a Multi-Level Factor Structure," CREATES Research Papers 2016-31, Department of Economics and Business Economics, Aarhus University.
    9. Fernandes, Leonardo H.S. & Silva, José W.L. & de Araujo, Fernando H.A. & Tabak, Benjamin M., 2023. "Multifractal cross-correlations between green bonds and financial assets," Finance Research Letters, Elsevier, vol. 53(C).
    10. Robinson, Peter M. & Velasco, Carlos, 2018. "Inference on trending panel data," Journal of Econometrics, Elsevier, vol. 206(2), pages 282-304.
    11. 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.
    12. Ergemen, Yunus Emre, 2023. "Parametric estimation of long memory in factor models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1483-1499.
    13. 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.
    14. Yunus Emre Ergemen, 2016. "Generalized Efficient Inference on Factor Models with Long-Range Dependence," CREATES Research Papers 2016-05, Department of Economics and Business Economics, Aarhus University.
    15. Ergemen, Yunus Emre & Haldrup, Niels & Rodríguez-Caballero, Carlos Vladimir, 2016. "Common long-range dependence in a panel of hourly Nord Pool electricity prices and loads," Energy Economics, Elsevier, vol. 60(C), pages 79-96.
    16. Arnoldo López-Marmolejo & Carlos Vladimir Rodríguez-Caballero & Daniel Ventosa-Santaulà ria, 2021. "Remittances at record highs in Latin America: Time to revisit the Dutch disease," Economics Bulletin, AccessEcon, vol. 41(3), pages 2133-2146.
    17. Yunus Emre Ergemen & Carlos Vladimir Rodríguez-Caballero, 2016. "A Dynamic Multi-Level Factor Model with Long-Range Dependence," CREATES Research Papers 2016-23, Department of Economics and Business Economics, Aarhus University.
    18. C. Vladimir Rodríguez-Caballero & J. Eduardo Vera-Valdés, 2021. "Air Pollution and Mobility, What Carries COVID-19?," Econometrics, MDPI, vol. 9(4), pages 1-17, October.
    19. Yunus Emre Ergemen, 2016. "System Estimation of Panel Data Models under Long-Range Dependence," CREATES Research Papers 2016-02, Department of Economics and Business Economics, Aarhus University.
    20. Yuichi Goto & Koichi Arakaki & Yan Liu & Masanobu Taniguchi, 2023. "Homogeneity tests for one-way models with dependent errors under correlated groups," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(1), pages 163-183, March.
    21. Yunus Emre Ergemen, 2022. "Parametric Estimation of Long Memory in Factor Models," CREATES Research Papers 2022-10, Department of Economics and Business Economics, Aarhus University.

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    More about this item

    Keywords

    Fractional cointegration; Factor models; Long memory; Realized volatility;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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