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EFFICIENCY IN LARGE DYNAMIC PANEL MODELS WITH COMMON FACTORS

Published online by Cambridge University Press:  16 April 2014

Patrick Gagliardini  and
Christian Gourieroux
Patrick Gagliardini*
Affiliation:
Università della Svizzera Italiana
Christian Gourieroux
Affiliation:
CREST (Paris) and University of Toronto
*
*Address correspondence to Patrick Gagliardini, Università della Svizzera Italiana, Faculty of Economics, Via Buffi 13, CH-6900 Lugano, Switzerland; e-mail: patrick.gagliardini@usi.ch.
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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.

Type
ARTICLES
Information
Econometric Theory , Volume 30 , Issue 5 , October 2014 , pp. 961 - 1020
Copyright
Copyright © Cambridge University Press 2014 

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