VAR.BaBPR: Bootstrap-after-Bootstrap Prediction Intervals for VAR(p)...

In VAR.etp: VAR Modelling: Estimation, Testing, and Prediction

Bootstrap-after-Bootstrap Prediction Intervals for VAR(p) Model

Description

Bias-correction given with stationarity Correction

Usage

VAR.BaBPR(x, p, h, nboot = 500, nb = 200, type = "const", alpha = 0.95)

Arguments

x	data matrix in column
p	AR order
h	forecasting period
nboot	number of 2nd-stage bootstrap iterations
nb	number of 1st-stage bootstrap iterations
type	"const" for the AR model with intercept only, "const+trend" for the AR model with intercept and trend
alpha	100(1-alpha) percent prediction intervals

Details

Bias-correction given with stationarity Correction

Value

Intervals	Prediction Intervals
Forecast	Point Forecasts
alpha	Probability Content of Prediction Intervals

Note

Bias-correction given with stationarity Correction

Author(s)

Jae H. Kim

References

Kim, J. H. (2001). Bootstrap-after-bootstrap prediction intervals for autoregressive models, Journal of Business & Economic Statistics, 19, 117-128.

Examples

data(dat)
VAR.BaBPR(dat,p=2,h=10,nboot=200,nb=100,type="const",alpha=0.95)
# nboot and nb are set to low numbers for fast execution in the example
# In actual implementation, use higher numbers such as nboot=1000, nb=200

VAR.etp documentation built on Aug. 31, 2023, 9:08 a.m.