The R package QregBB accompanies the paper:
Gregory, K.B., Lahiri, S.N., Nordman, D.J. (2018). A smooth block bootstrap for quantile regression with time series. Annals of Statistics 46(3), 1138-1166
Find the paper at https://projecteuclid.org/euclid.aos/1525313078.
You can install the development version of QregBB from GitHub with:
# install.packages("devtools")
devtools::install_github("gregorkb/QregBB")The main function in the package is QregBB, which performs the MBB,
SMBB, ETBB, and SETBB bootstrap procedures (all at once) for estimating
the sampling distributions of quantile regression estimators with time
series data.
library(QregBB)
n <- 50
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
#>
#> Call:
#> QregBB(Y = Y, X = X, tau = 0.5, l = 4, B = 500, h = NULL, alpha = 0.05)
#>
#> Coefficients:
#> Estimate SE (MBB) SE (SMBB) SE (ETBB) SE (SETBB)
#> (Intercept) -1.19048 0.30832 0.35614 0.29801 0.34243
#> beta_1 1.37104 0.14455 0.16978 0.13906 0.16874
#> beta_2 0.00682 0.18293 0.24371 0.20035 0.24457
#>
#> Confidence intervals:
#> Estimate lower (MBB) upper (MBB) lower (SMBB) upper (SMBB)
#> (Intercept) -1.19048 -2.01656 -0.78747 -1.93770 -0.61313
#> beta_1 1.37104 1.00985 1.56780 1.00313 1.64540
#> beta_2 0.00682 -0.44854 0.28923 -0.47015 0.45646
#> lower (ETBB) upper (ETBB) lower (SETBB) upper (SETBB)
#> (Intercept) -1.97114 -0.76846 -1.83370 -0.58475
#> beta_1 1.03540 1.59665 0.96382 1.67961
#> beta_2 -0.49587 0.29042 -0.47396 0.43994The function getNPPIblksizesQR implements the block size selection
method described in Gregory et al. (2018) for MBB, SMBB, ETBB, and
SETBB.
blksize.out <- getNPPIblksizesQR(Y,X,tau=.5)
blksize.out
#> $l.opt.MBB
#> [1] 23
#>
#> $l.opt.ETBB
#> [1] 3
#>
#> $l.opt.SMBB
#> [1] 25
#>
#> $l.opt.SETBB
#> [1] 3