# Tboot: Bootstrap-t Confidence Interval (Wild Bootstrap) - Linear... In hcci: Interval Estimation of Linear Models with Heteroskedasticity

 Tboot R Documentation

## Bootstrap-t Confidence Interval (Wild Bootstrap) - Linear Models Heteroskedasticity

### Description

This function calculates confidence intervals for the parameters in heteroskedasticity linear regression models. Ranges are estimated by the bootstrap-t and double bootstrap-t.

### Usage

``````Tboot(model, significance=0.05, hc=4, double=FALSE, J=NULL, K=NULL,
``````

### Arguments

 `model` Any object of class `lm`; `significance` Significance level of the test. By default, the level of significance is `0.05`; `hc` Method HC that will be used to estimate the covariance structure. The argument `method` may be `0`, `2`, `3`, `4` or `5`; `double` If `double = TRUE` will be calculated intervals bootstrap-t and double bootstrap-t. The default is `double = FALSE`; `J` Number of replicas of the first bootstrap; `K` Number of replicas of the second bootstrap; `distribution` Distribution of the random variable with mean zero and variance one. This random variable multiplies the error estimates in the generation of the samples. The argument `distribution` can be rademacher or normal (standard normal). The default is `distribution = rademacher`.

### Value

A list with the following components:

 `beta` A numeric vector of length 2 containing the estimated coefficients of the model. `ci_lower_simple` A numeric vector of length 2 containing the lower bounds of the simple bootstrap confidence intervals for the coefficients. `ci_upper_simple` A numeric vector of length 2 containing the upper bounds of the simple bootstrap confidence intervals for the coefficients. `ci_lower_double` A logical vector of length 0 or 2. If 'double = FALSE', this will be a logical vector of length 0. If 'double = TRUE', this will be a numeric vector containing the lower bounds of the double bootstrap confidence intervals for the coefficients. `ci_upper_double` A logical vector of length 0 or 2. If 'double = FALSE', this will be a logical vector of length 0. If 'double = TRUE', this will be a numeric vector containing the upper bounds of the double bootstrap confidence intervals for the coefficients. `J` A numeric value indicating the number of bootstrap resamples used in the simple bootstrap. `K` A numeric value indicating the number of bootstrap resamples used in the double bootstrap, if 'double = TRUE'.

### Author(s)

Pedro Rafael Diniz Marinho <pedro.rafael.marinho@gmail.com>

### References

Booth, J.G. and Hall, P. (1994). Monte Carlo approximation and the iterated bootstrap. Biometrika, 81, 331-340.

Cribari-Neto, F.; Lima, M.G. (2009). Heteroskedasticity-consistent interval estimators. Journal of Statistical Computation and Simulation, 79, 787-803;

Wu, C.F.J. (1986). Jackknife, bootstrap and other resampling methods in regression analysis, 14, 1261-1295;

McCullough, B.D; Vinod, H.D. (1998). Implementing the double bootstrap, 12, 79-95.

`Pboot`.

### Examples

``````data(schools)
datas = schools[-50,]
y = datas\$Expenditure
x = datas\$Income/10000
model = lm(y ~ x)
Tboot(model=model, significance = 0.05, hc = 4, double = FALSE,
J=1000, K = 100, distribution = "rademacher")
``````

hcci documentation built on May 29, 2024, 5:37 a.m.