tvReg (version 0.5.6)

confint.tvlm: 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 tvlm
confint(
  object,
  parm,
  level = 0.95,
  runs = 100,
  tboot = c("wild", "wild2"),
  ...
)
# S3 method for tvar
confint(
  object,
  parm,
  level = 0.95,
  runs = 100,
  tboot = c("wild", "wild2"),
  ...
)
# S3 method for tvsure
confint(
  object,
  parm,
  level = 0.95,
  runs = 100,
  tboot = c("wild", "wild2"),
  ...
)
# S3 method for tvvar
confint(
  object,
  parm,
  level = 0.95,
  runs = 100,
  tboot = c("wild", "wild2"),
  ...
)
# S3 method for tvirf
confint(
  object,
  parm,
  level = 0.95,
  runs = 100,
  tboot = c("wild", "wild2"),
  ...
)
# S3 method for 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 \& Economic Statistics, online, 1-13.

Mammen, E (1993) Bootstrap and wild bootstrap for high dimensional linear models, Annals of Statistics, 21, 255-285.

See Also

tvLM, tvAR, tvVAR, tvSURE

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)
# }