getNPPIblksizesQR: Chooses block sizes for MBB, ETBB, SMBB, and SETBB via the...

In gregorkb/QregBB: Block Bootstrap Methods for Quantile Regression in Time Series

Chooses block sizes for MBB, ETBB, SMBB, and SETBB via the NPPI for quantile regression

Description

Chooses block sizes for MBB, ETBB, SMBB, and SETBB via the NPPI for quantile regression

Usage

getNPPIblksizesQR(Y, X, tau, min.in.JAB = 100)

Arguments

Y	the vector of response values.
X	the design matrix (including a column of ones for the intercept).
tau	the quantile of interest.
min.in.JAB	the minimum number of Monte-Carlos draws desired in each jackknife draw

Details

This function is based on the nonparametric plug-in (NPPI) method discussed in Lahiri (2003), which makes use of the jackknife-after-bootstrap (JAB).

Value

Returns a list of the NPPI-selected block sizes for the MBB, SMBB, ETBB, and SETBB.

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. (2003). Resampling Methods for Dependent Data. Springer, New York.

Examples

# generate some data and use NPPI to choose block sizes for MBB, SMBB, ETBB, and SETBB.
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)

blksize.out <- getNPPIblksizesQR(Y,X,tau=.5)
blksize.out

gregorkb/QregBB documentation built on June 7, 2022, 12:31 p.m.