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Estimation of Fractionally Integrated Panels with Fixed Effects and Cross-Section Dependence

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  • Yunus Emre Ergemen

    () (Aarhus University and CREATES)

  • Carlos Velasco

    () (Universidad Carlos III de Madrid)

Abstract

We consider large N, T panel data models with fixed effects, common factors allowing cross-section dependence, and persistent data and shocks, which are assumed fractionally integrated. In a basic setup, the main interest is on the fractional parameter of the idiosyncratic component, which is estimated in first differences after factor removal by projection on the cross-section average. The pooled conditional-sum-of-squares estimate is root-NT consistent but the normal asymptotic distribution might not be centered, requiring the time series dimension to grow faster than the cross-section size for correction. Generalizing the basic setup to include covariates and heterogeneous parameters, we propose individual and common-correlation estimates for the slope parameters, while error memory parameters are estimated from regression residuals. The two parameter estimates are root-T consistent and asymptotically normal and mutually uncorrelated, irrespective of possible cointegration among idiosyncratic components. A study of small-sample performance and an empirical application to realized volatility persistence are included.

Suggested Citation

  • Yunus Emre Ergemen & Carlos Velasco, 2015. "Estimation of Fractionally Integrated Panels with Fixed Effects and Cross-Section Dependence," CREATES Research Papers 2015-35, Department of Economics and Business Economics, Aarhus University.
  • Handle: RePEc:aah:create:2015-35
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Robinson, Peter M. & Velasco, Carlos, 2018. "Inference on trending panel data," Journal of Econometrics, Elsevier, vol. 206(2), pages 282-304.
    2. Tobias Hartl & Roland Weigand, 2018. "Multivariate Fractional Components Analysis," Papers 1812.09149, arXiv.org, revised Jan 2019.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. Ergemen, Yunus Emre & Rodríguez Caballero, Carlos Vladimir, 2017. "Estimation of a Dynamic Multilevel Factor Model with possible long-range dependence," DES - Working Papers. Statistics and Econometrics. WS 24614, Universidad Carlos III de Madrid. Departamento de Estadística.

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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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