VAR.BaBPR {VAR.etp} | R Documentation |
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
[Package VAR.etp version 1.1 Index]