Template-Type: ReDIF-Paper 1.0
Author-Name: Russel Davidson
Author-X-Name-First: Russel 
Author-X-Name-Last: Davidson
Author-Email: russel.davidson@mcgill.ca
Author-Workplace-Name: Department of Economics and CIREQ McGill University
Author-Name: Andrea Monticini
Author-X-Name-First: Andrea 
Author-X-Name-Last: Monticini
Author-Email: andrea.monticini@gmail.com
Author-Workplace-Name: Dipartimento di Economia e Finanza, Università Cattolica del Sacro Cuore
Title: Heteroskedasticity-and-Autocorrelation-Consistent Bootstrapping
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.
Length: 18
Creation-Date: 2014-03
File-URL: http://dipartimenti.unicatt.it/economia-finanza-def012.pdf
File-Format: Application/pdf
File-Function: First version, 2014
Number: def012
Classification-JEL: C12, C22, C32
Keywords: Bootstrap, time series, wild bootstrap, dependent wild bootstrap,HAC covariance matrix estimator
Handle: RePEc:ctc:serie1:def012