This paper deals with asymptotically efficient estimation in exchangeable nonlinear dynamic
panel models with common unobservable factor. These models are especially relevant for applications to large portfolios of credits, corporate bonds, or life insurance contracts, and are 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 macro-dynamics, induced by the lagged individual observation and the common stochastic factor, respectively. For large cross-sectional and time dimensions n and T, respectively, we derive the efficiency bound and introduce computationally simple efficient estimators for both the
micro- and macro-parameters. In particular, we show that the fixed effects estimator of the microparameter is asymptotically efficient. The results are based on an asymptotic expansion of the
log-likelihood function in powers of 1=n. The results are illustrated with the stochastic migration
model for credit risk analysis.