Università Cattolica del Sacro Cuore

N. 12 - "Heteroskedasticity-and-Autocorrelation-Consistent Bootstrapping" - Russell Davidson and Andrea Monticini

01 gennaio 2014

R. Davidson and A. Monticini


In many, if not most, econometric applications, it is impossible to estimate consistently the elements of the white-noise process or processes that underlie the DGP. A common example is a regression model with heteroskedastic and/or autocorrelated disturbances, where the heteroskedasticity and autocorrelation are of unknown form.
A particular version of the wild bootstrap can be shown to work very well with many models, both univariate and multivariate, in the presence of heteroskedasticity. Nothing comparable appears to exist for handling serial correlation. Recently, there has been proposed something called the dependent wild bootstrap. Here, we extend this new method, and link it to the well-known HAC covariance estimator, in much the same way as one can link the wild bootstrap to the HCCME. It works very well even with sample sizes smaller than 50, and merits considerable further study.

Keywords: Bootstrap, time series, wild bootstrap, dependent wild bootstrap, HAC covariance matrix estimator

JEL Codes: C12, C22, C32


Autore: Paul Bingley and Lorenzo Cappellari

Anno: 0