EWS (version 0.2.0)

BlockBootstrapp: Block Bootstrapp

Description

This function enables the estimation of the block size for resampling. The size of the blocks is computed as in Hall, Horowitz and Jing (1995). Then, the function returns in a matrix the new resampled input variables. These variables are then used to determine the confidence intervals of the response functions proposed by Lajaunie (2021).

Usage

BlockBootstrapp(Dicho_Y, Exp_X, Intercept, n_simul)

Arguments

Dicho_Y

Vector of the binary time series.

Exp_X

Vector or Matrix of explanatory time series.

Intercept

Boolean value: TRUE for an estimation with intercept, and FALSE otherwise.

n_simul

Numeric variable equal to the total number of replications.

Value

A matrix containing the replications of the new resampled input variables. The matrix contains \(n \times S\) colomns, where \(n\) denotes the number of input variables, and \(S\) denotes the number of replications.

References

Hall, Peter, Joel L. Horowitz, and Bing-Yi Jing. "On blocking rules for the bootstrap with dependent data." Biometrika 82.3 (1995): 561-574.

Lajaunie, Quentin. Generalized Impulse Response Function for Dichotomous Models. No. 2852. Orleans Economics Laboratory/Laboratoire d'Economie d'Orleans (LEO), University of Orleans, 2021.

Examples

# NOT RUN {
# NOT RUN {

# Import data
data("data_USA")

# Data process
Var_Y <- as.vector(data_USA$NBER)
Var_X <- as.vector(data_USA$Spread)

# Resample
results <- BlockBootstrapp(Dicho_Y = Var_Y, Exp_X = Var_X, Intercept = TRUE, n_simul = 100)

# print results
results

#}
# }