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Efficiency in Large Dynamic Panel Models with Common Factor

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  • Patrick GAGLIARDINI

    (Crest)

  • Christian GOURIEROUX

    (Crest)

Abstract

This paper deals with asymptotically efficient estimation in exchangeable nonlinear dynamicpanel models with common unobservable factor. These models are especially relevantfor applications to large portfolios of credits, corporate bonds, or life insurance contracts, andare recommended in the current regulation in Finance (Basel II and Basel III) and Insurance(Solvency II). The specification accounts for both micro- and macro-dynamics, induced bythe lagged individual observation and the common stochastic factor, respectively. For largecross-sectional and time dimensions n and T, respectively, we derive the efficiency boundand introduce computationally simple efficient estimators for both the micro- and macroparameters.In particular, we show that the fixed effects estimator of the micro-parameteris asymptotically efficient. The results are based on an asymptotic expansion of the loglikelihoodfunction in powers of 1=n. This expansion is used to investigate the second-orderbias properties of the estimators. The results are illustrated with the stochastic migrationmodel for credit risk analysis.

Suggested Citation

  • Patrick GAGLIARDINI & Christian GOURIEROUX, 2010. "Efficiency in Large Dynamic Panel Models with Common Factor," Working Papers 2010-05, Center for Research in Economics and Statistics.
  • Handle: RePEc:crs:wpaper:2010-05
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    Cited by:

    1. Matteo Barigozzi & Brownlees Christian & Gallo Giampiero & David Veredas, "undated". "Disentangling systematic and idiosyncratic risks for large panels of assets," ULB Institutional Repository 2013/136237, ULB -- Universite Libre de Bruxelles.
    2. repec:eee:econom:v:201:y:2017:i:2:p:176-197 is not listed on IDEAS
    3. Barigozzi, Matteo & Brownlees, Christian & Gallo, Giampiero M. & Veredas, David, 2014. "Disentangling systematic and idiosyncratic dynamics in panels of volatility measures," Journal of Econometrics, Elsevier, vol. 182(2), pages 364-384.
    4. Chen, Liang & Dolado, Juan J. & Gonzalo, Jesús, 2014. "Detecting big structural breaks in large factor models," Journal of Econometrics, Elsevier, vol. 180(1), pages 30-48.
    5. Carlos Perez Montes, 2015. "Estimation of Regulatory Credit Risk Models," Journal of Financial Services Research, Springer;Western Finance Association, vol. 48(2), pages 161-191, October.
    6. Francesco Audrino & Fulvio Corsi & Kameliya Filipova, 2016. "Bond Risk Premia Forecasting: A Simple Approach for Extracting Macroeconomic Information from a Panel of Indicators," Econometric Reviews, Taylor & Francis Journals, vol. 35(2), pages 232-256, February.
    7. Dhaene, Geert & Jochmans, Koen, 2016. "Likelihood Inference In An Autoregression With Fixed Effects," Econometric Theory, Cambridge University Press, vol. 32(05), pages 1178-1215, October.
    8. Torben G. Andersen & Nicola Fusari & Viktor Todorov & Rasmus T. Varneskov, 2018. "Unified Inference for Nonlinear Factor Models from Panels with Fixed and Large Time Span," CREATES Research Papers 2018-03, Department of Economics and Business Economics, Aarhus University.

    More about this item

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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