cocoBoot: Bootstrap Based Model Assessment Procedure

In coconots: Convolution-Closed Models for Count Time Series

Bootstrap Based Model Assessment Procedure

Description

Model checking procedure emphasising reproducibility in fitted models, as proposed by Tsay (1992).

Usage

cocoBoot(
  coco,
  numb.lags = 21,
  rep.Bootstrap = 1000,
  conf.alpha = 0.05,
  julia = FALSE,
  julia_seed = NULL
)

Arguments

coco	An object of class coco
numb.lags	Number of lags for which to compute sample autocorrelations (default: 21).
rep.Bootstrap	Number of bootstrap replicates to use (default: 1000)
conf.alpha	100(1-\code{conf.alpha})\% probability interval for the acceptance envelopes (default: 0.05)
julia	if TRUE, the bootstrap is run with julia (default: FALSE)
julia_seed	Seed for the julia implementation. Only used if julia equals TRUE

Details

Bootstrap-generated acceptance envelopes for the autocorrelation function provides an overall evaluation by comparing it with the sample autocorrelation function in a joint plot.

Value

an object of class cocoBoot. It contains the bootstrapped confidence intervals of the autocorrelations and information on the model specifications.

References

Tsay, R. S. (1992) Model checking via parametric bootstraps in time series analysis. Applied Statistics 41, 1–15.

Examples

lambda <- 1
alpha <- 0.4
set.seed(12345)
data <- cocoSim(order = 1, type = "Poisson", par = c(lambda, alpha), length = 100)
fit <- cocoReg(order = 1, type = "Poisson", data = data)

# bootstrap model assessment - R implementation
boot_r <- cocoBoot(fit, rep.Bootstrap=400)
plot(boot_r)

coconots documentation built on Aug. 22, 2025, 5:17 p.m.