Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/170609
Authors: 
Davidson, Russel
Monticini, Andrea
Year of Publication: 
2014
Series/Report no.: 
Working Paper, Dipartimento di Economia e Finanza (DISCE), Università Cattolica del Sacro Cuore 12
Abstract: 
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.
Subjects: 
Bootstrap
time series
wild bootstrap
dependent wild bootstrap
HAC covariance matrix estimator
JEL: 
C12
C22
C32
Document Type: 
Working Paper

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