Bootstrapping two-stage quasi-maximum likelihood estimators of time series models*

This article provides results on the validity of bootstrap inference methods for two-stage quasi-maximum likelihood estimation involving time series data, such as those used multivariate volatility models or copula-based models. Existing approaches require researcher to compute and combine many first- second-order derivatives, which can be difficult do is susceptible error. Bootstrap are simpler apply, allowing substitution capital (CPU cycles) labor (keeping track derivatives). We show consistency distribution variance estimators, thereby justifying use percentile intervals standard errors.