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Panel Estimation for Worriers

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  • Markus Eberhardt
  • Anindya Banerjee and J. James Reade

Abstract

The recent blossoming of panel econometrics in general and panel time-series methods in particular has enabled many more research questions to be investigated than before. However, this development has not assuaged serious concerns over the lack of diagnostic testing procedures in panel econometrics, in particular vis-a-vis the prominence of such practices in the time-series domain: the recent introduction of residual cross-section independence tests aside, within mainstream panel empirics the combination of 'model', 'spefication' and 'testing' typically refers to the distinction between fixed and random effects, as opposed to a rigorous investigation of residual properties. In this paper we investigate these issues in the context of non-stationary panels with multifactor error structure, employing Monte Carlo simulations to investigate the distributions and rejection frequencies for standard time-series diagnostic procedures, including tests for residual autocorrelation, ARCH, normality, heteroskedasticity and functional form.

Suggested Citation

  • Markus Eberhardt & Anindya Banerjee and J. James Reade, 2010. "Panel Estimation for Worriers," Economics Series Working Papers 514, University of Oxford, Department of Economics.
    • Handle: RePEc:oxf:wpaper:514
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    1. Lozano, Ignacio & Julio, Juan Manuel, 2016. "Descentralización fiscal y crecimiento económico en Colombia: evidencia de datos de panel a nivel regional," Revista CEPAL, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL), August.
    2. Anindya Banerjee & Josep Lluis Carrion-i-Silvestre, 2011. "Testing for Panel Cointegration Using Common Correlated Effects," Discussion Papers 11-16, Department of Economics, University of Birmingham.
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    4. Bakas, Dimitrios & Papapetrou, Evangelia, 2014. "Unemployment in Greece: Evidence from Greek regions using panel unit root tests," The Quarterly Review of Economics and Finance, Elsevier, vol. 54(4), pages 551-562.
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    6. Anindya Banerjee & Josep Lluís Carrion-i-Silvestre, 2017. "Testing for Panel Cointegration Using Common Correlated Effects Estimators," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(4), pages 610-636, July.
    7. Evangelia Papapetrou & Dimitrios Bakas, 2012. "Unemployment in Greece: evidence from Greek regions," Working Papers 146, Bank of Greece.
    8. Roth, Felix & Gros, Daniel & Nowak-Lehmann D., Felicitas, 2012. "Has the financial crisis eroded citizens' trust in the European Central Bank? Panel data evidence for the Euro area, 1999-2011," Center for European, Governance and Economic Development Research Discussion Papers 124, University of Goettingen, Department of Economics.
    9. Lozano, Ignacio & Julio, Juan Manuel, 2016. "Fiscal decentralization and economic growth in Colombia: evidence from regional-level panel data," Revista CEPAL, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL), August.
    10. Dierk Herzer & Holger Strulik & Sebastian Vollmer, 2012. "The long-run determinants of fertility: one century of demographic change 1900–1999," Journal of Economic Growth, Springer, vol. 17(4), pages 357-385, December.
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    14. Georg von Graevenitz & Christian Helmers & Valentine Millot & Oliver Turnbull, 2016. "Does Online Search Predict Sales? Evidence from Big Data for Car Markets in Germany and the UK," Working Papers 71, Queen Mary, University of London, School of Business and Management, Centre for Globalisation Research.
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    More about this item

    Keywords

    Panel time-series; residual diagnostics; common factor model;

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • 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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