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

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Description

Bias-correction given with stationarity Correction

Usage

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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

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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