We apply the generalized cluster bootstrap to both Gaussian quasi-likelihood and robust estimates in the context of highly unbalanced clustered data. We compare it with the transformation bootstrap where the data are generated by the random effect and transformation models and all the random...

An indirect inference method is implemented for a class of stochastic volatility models for financial data based on non-Gaussian Ornstein–Uhlenbeck (OU) processes. First, a quasi-likelihood estimator is derived from an approximative Gaussian state space representation of the OU model. Next,...

In view of the cumbersome and often intractable numerical integrations required for a full likelihood analysis, several suggestions have been made recently for approximate inference in generalized linear mixed models (GLMMs). Two closely related approximate methods are the penalized...

This paper presents a framework for using high frequency derivative prices to estimate the drift of generalized security price processes. This work may be seen more generally as a quasi-likelihood approach to estimating continuous-time parameters of derivative pricing models using discrete...