Bootstrapping Two-Stage Quasi-Maximum Likelihood Estimators of Time Series Models

Abstract 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 for multivariate volatility models or copula-based models. Existing approaches require the researcher to compute and combine many first- and second-order derivatives, which can be difficult to do and is susceptible to error. Bootstrap methods are simpler to apply, allowing the substitution of capital (CPU cycles) for labor (keeping track of derivatives). We show the consistency of the bootstrap distribution and consistency of bootstrap variance estimators, thereby justifying the use of bootstrap percentile intervals and bootstrap standard errors.

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