Maximum likelihood estimation for score-driven models

We establish strong consistency and asymptotic normality of the maximum likelihood estimator for stochastic time-varying parameter models driven by score predictive conditional function. For this purpose, we formulate primitive conditions global identification, invertibility, consistency, both under correct specification misspecification model. A detailed illustration is provided a volatility model with disturbances from Student’s t distribution.