confint.tvlm {tvReg} | R Documentation |
Confidence Intervals for Objects in tvReg
Description
confint is used to estimate the bootstrap confidence intervals for objects with class
attribute tvlm
, tvar
, tvirf
, tvsure
and tvplm
.
Usage
## S3 method for class 'tvlm'
confint(
object,
parm,
level = 0.95,
runs = 100,
tboot = c("wild", "wild2"),
...
)
## S3 method for class 'tvar'
confint(
object,
parm,
level = 0.95,
runs = 100,
tboot = c("wild", "wild2"),
...
)
## S3 method for class 'tvsure'
confint(
object,
parm,
level = 0.95,
runs = 100,
tboot = c("wild", "wild2"),
...
)
## S3 method for class 'tvvar'
confint(
object,
parm,
level = 0.95,
runs = 100,
tboot = c("wild", "wild2"),
...
)
## S3 method for class 'tvirf'
confint(
object,
parm,
level = 0.95,
runs = 100,
tboot = c("wild", "wild2"),
...
)
## S3 method for class 'tvplm'
confint(
object,
parm,
level = 0.95,
runs = 100,
tboot = c("wild", "wild2"),
...
)
Arguments
object |
An object used to select a method. |
parm |
A specification of which parameters are to be given confidence intervals, either a vector of numbers or a vector of names. If missing, all parameters are considered. |
level |
Numeric, the confidence level required (between 0 and 1). |
runs |
(optional) Number of bootstrap replications. |
tboot |
Type of wild bootstrap, choices 'wild'(default), 'wild2'. Option 'wild' uses the distribution suggested by Mammen (1993) in the wild resampling, while 'wild2' uses the standard normal. |
... |
Other parameters passed to specific methods. |
Value
an object of class tvsure
with BOOT, Lower and Upper different from NULL.
References
Chen, X. B., Gao, J., Li, D., and Silvapulle, P (2017) Nonparametric estimation and forecasting for time-varying coefficient realized volatility models, Journal of Business and Economic Statistics, 36, 88-100.
Mammen, E (1993) Bootstrap and wild bootstrap for high dimensional linear models, Annals of Statistics, 21, 255-285.
See Also
Examples
## Not run:
##Calculation of confidence intervals for a TVLM model
##Generation of time-varying coefficients linear model
set.seed(42)
tau <- seq(1:200)/200
beta <- data.frame(beta1 = sin(2*pi*tau), beta2= 2*tau)
X1 <- rnorm(200)
X2 <- rchisq(200, df = 4)
error <- rt(200, df = 10)
y <- apply(cbind(X1, X2)*beta, 1, sum) + error
data <- data.frame(y = y, X1 = X1, X2 = X2)
##Fitting the model and confidence interval calculation
model.tvlm <- tvLM(y ~ 0 + X1 + X2, data = data, bw = 0.29)
tvci <- confint(model.tvlm, level = 0.95, runs = 20)
##If a second confidence interval on the "same" object is calculated,
##for example with a different level, the calculation is faster
tvci.80 <- confint(tvci, level = 0.8)
## End(Not run)