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Efficiency In Large Dynamic Panel Models With Common Factors

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  • Gagliardini, Patrick
  • Gourieroux, Christian

Abstract

This paper deals with asymptotically efficient estimation in exchangeable nonlinear dynamic panel models with common unobservable factors. These models are relevant for applications to large portfolios of credits, corporate bonds, or life insurance contracts. For instance, the Asymptotic Risk Factor (ARF) model is recommended in the current regulation in Finance (Basel II and Basel III) and Insurance (Solvency II) for risk prediction and computation of the required capital. The specification accounts for both micro- and macrodynamics, induced by the lagged individual observations and the common stochastic factors, respectively. For large cross-sectional and time dimensions n and T, we derive the efficiency bound and introduce computationally simple efficient estimators for both the micro- and macroparameters. The results are based on an asymptotic expansion of the log-likelihood function in powers of 1/n, and are linked to granularity theory. The results are illustrated with the stochastic migration model for credit risk analysis.

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  • Gagliardini, Patrick & Gourieroux, Christian, 2014. "Efficiency In Large Dynamic Panel Models With Common Factors," Econometric Theory, Cambridge University Press, vol. 30(5), pages 961-1020, October.
  • Handle: RePEc:cup:etheor:v:30:y:2014:i:05:p:961-1020_00
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    Cited by:

    1. Matteo Barigozzi & Christian T. Brownlees & Giampiero M. Gallo & David Veredas, 2010. "Disentangling Systematic and Idiosyncratic Risk for Large Panels of Assets," Econometrics Working Papers Archive wp2010_06, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    2. Gagliardini, Patrick & Gouriéroux, Christian, 2017. "Double instrumental variable estimation of interaction models with big data," Journal of Econometrics, Elsevier, vol. 201(2), pages 176-197.
    3. Antoine Djogbenou & Christian Gouri'eroux & Joann Jasiak & Maygol Bandehali, 2021. "Composite Likelihood for Stochastic Migration Model with Unobserved Factor," Papers 2109.09043, arXiv.org.
    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. Dhaene, Geert & Jochmans, Koen, 2016. "Likelihood Inference In An Autoregression With Fixed Effects," Econometric Theory, Cambridge University Press, vol. 32(5), pages 1178-1215, October.
    6. Dhaene, Geert & Jochmans, Koen, 2016. "Likelihood Inference In An Autoregression With Fixed Effects," Econometric Theory, Cambridge University Press, vol. 32(5), pages 1178-1215, October.
    7. Andersen, Torben G. & Fusari, Nicola & Todorov, Viktor & Varneskov, Rasmus T., 2019. "Unified inference for nonlinear factor models from panels with fixed and large time span," Journal of Econometrics, Elsevier, vol. 212(1), pages 4-25.
    8. 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.
    9. 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.
    10. Elena Andreou & Patrick Gagliardini & Eric Ghysels & Mirco Rubin, 2016. "Is Industrial Production Still the Dominant Factor for the US Economy?," Swiss Finance Institute Research Paper Series 16-11, Swiss Finance Institute.
    11. Gregory Connor & Robert A. Korajczyk, 2019. "Semi-strong factors in asset returns," Economics Department Working Paper Series n294-19.pdf, Department of Economics, National University of Ireland - Maynooth.
    12. 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.
    13. Dhaene, Geert & Jochmans, Koen, 2016. "Likelihood Inference In An Autoregression With Fixed Effects," Econometric Theory, Cambridge University Press, vol. 32(5), pages 1178-1215, October.
    14. Gagliardini, Patrick & Gouriéroux, Christian, 2019. "Identification by Laplace transforms in nonlinear time series and panel models with unobserved stochastic dynamic effects," Journal of Econometrics, Elsevier, vol. 208(2), pages 613-637.
    15. Kerem Tuzcuoglu, 2019. "Composite Likelihood Estimation of an Autoregressive Panel Probit Model with Random Effects," Staff Working Papers 19-16, Bank of Canada.

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    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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