BlockBootstrapp {EWS} | R Documentation |
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).
BlockBootstrapp(Dicho_Y, Exp_X, Intercept, n_simul)
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. |
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.
Jean-Baptiste Hasse and Quentin Lajaunie
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.
# 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
#}