| QregBB-package {QregBB} | R Documentation |
Block Bootstrap Methods for Quantile Regression in Time Series
Description
Implements moving-blocks bootstrap and extended tapered-blocks bootstrap, as well as smooth versions of each, for quantile regression in time series. This package accompanies the paper: Gregory, K. B., Lahiri, S. N., & Nordman, D. J. (2018). A smooth block bootstrap for quantile regression with time series. The Annals of Statistics, 46(3), 1138-1166.
Details
Implements moving-blocks bootstrap and extended tapered-blocks bootstrap, as well as smooth versions of each, for quantile regression in time series. This package accompanies Gregory et al. (2018).
| Package: | QregBB |
| Type: | Package |
| Title: | Block Bootstrap Methods for Quantile Regression in Time Series |
| Version: | 1.0.0 |
| Date: | 2022-06-01 |
| Author: | Karl Gregory |
| Maintainer: | Karl Gregory <gregorkb@stat.sc.edu> |
| Description: | Implements moving-blocks bootstrap and extended tapered-blocks bootstrap, as well as smooth versions of each, for quantile regression in time series. This package accompanies the paper: Gregory, K. B., Lahiri, S. N., & Nordman, D. J. (2018). A smooth block bootstrap for quantile regression with time series. The Annals of Statistics, 46(3), 1138-1166. |
| License: | GPL-3 |
| RoxygenNote: | 7.2.0 |
| Imports: | quantreg |
Index of help topics:
QregBB Implements MBB, ETBB, SMBB, and SETBB for
quantile regression
QregBB-package Block Bootstrap Methods for Quantile Regression
in Time Series
getNPPIblksizesQR Chooses block sizes for MBB, ETBB, SMBB, and
SETBB via the NPPI for quantile regression
The main function is the QregBB function, which implements the moving-blocks bootstrap (MBB), the extended tapered-blocks bootstrap (ETBB), and smooth versions of each (SMBB, SETBB). The function getNPPIblksizesQR chooses the block size based on the non-parametric plug-in method described in Lahiri (2013). For the smooth methods, the bandwidth is chosen by using the function bw.SJ function on the fitted residuals; then the bandwidth matrix is the identity matrix times the value returned by bw.SJ.
Author(s)
Karl Gregory
Maintainer: Karl Gregory <gregorkb@stat.sc.edu>
References
Gregory, K. B., Lahiri, S. N., & Nordman, D. J. (2018). A smooth block bootstrap for quantile regression with time series. The Annals of Statistics, 46(3), 1138-1166.
Lahiri, S. N. (2013). Resampling methods for dependent data. Springer Science & Business Media.
Examples
n <- 100
X1 <- arima.sim(model=list(ar=c(.7,.1)),n)
X2 <- arima.sim(model=list(ar=c(.2,.1)),n)
e <- arima.sim(model=list(ar=c(.7,.1)),n)
Y <- X1 + e
X <- cbind(rep(1,n),X1,X2)
QregBB.out <- QregBB(Y,X,tau=.5,l=4,B=500,h=NULL,alpha=0.05)
QregBB.out