We propose a simulated maximum likelihood estimator for dynamic models based on non-parametric kernel methods. Our method is designed for models without latent dynamics from which one can simulate observations but cannot obtain a closed-form representation of the likelihood function. Using the...

We propose a simulated maximum likelihood estimator (SMLE) for general stochastic dynamic models based on nonparametric kernel methods. The method requires that, while the actual likelihood function cannot be written down, we can still simulate observations from the model. From the simulated...

Asymptotic properties of the quasi-maximum likelihood estimator (QMLE) for general ARCH(q) models - including for example Power ARCH and log-ARCH - are derived. Strong consistency is established under the assumptions that the ARCH process is geometrically ergodic, the conditional variance...