gas_bootstrap: Bootstrap GAS Model

In gasmodel: Generalized Autoregressive Score Models

Bootstrap GAS Model

Description

A function for bootstrapping coefficients of generalized autoregressive score (GAS) models of Creal et al. (2013) and Harvey (2013). Method "parametric" repeatedly simulates time series using the parametric model and re-estimates coefficients. Methods "simple_block", "moving_block", and "stationary_block" perform the standard variations of the circular block bootstrap. Instead of supplying arguments about the model, the function can be applied to the gas object obtained by the gas() function. The function enables parallelization.

Usage

gas_bootstrap(
  gas_object = NULL,
  method = "parametric",
  rep_boot = 1000L,
  block_length = NULL,
  quant = c(0.025, 0.975),
  y = NULL,
  x = NULL,
  distr = NULL,
  param = NULL,
  scaling = "unit",
  regress = "joint",
  p = 1L,
  q = 1L,
  par_static = NULL,
  par_link = NULL,
  par_init = NULL,
  lik_skip = 0L,
  coef_fix_value = NULL,
  coef_fix_other = NULL,
  coef_fix_special = NULL,
  coef_bound_lower = NULL,
  coef_bound_upper = NULL,
  coef_est = NULL,
  optim_function = wrapper_optim_nloptr,
  optim_arguments = list(opts = list(algorithm = "NLOPT_LN_NELDERMEAD", xtol_rel = 0,
    maxeval = 10000)),
  parallel_function = NULL,
  parallel_arguments = list()
)

Arguments

gas_object	An optional GAS estimate, i.e. a list of S3 class gas returned by function gas().
method	A method used for bootstrapping. Supported methods are "parametric", "simple_block", "moving_block", and "stationary_block".
rep_boot	A number of bootstrapping repetitions.
block_length	A length of blocks for methods "simple_block" and "moving_block". A mean length of blocks for method "stationary_block".
quant	A numeric vector of probabilities determining quantiles.
y, x, distr, param, scaling, regress, p, q, par_static, par_link, par_init, lik_skip, coef_fix_value, coef_fix_other, coef_fix_special, coef_bound_lower, coef_bound_upper, coef_est	When gas_object is not supplied, the estimated model can be specified using these individual arguments. See the arguments and value of the gas() function for more details.
optim_function	An optimization function. For suitable wrappers of common R optimization functions, see wrappers_optim.
optim_arguments	An optional list of arguments to be passed to the optimization function.
parallel_function	A parallelization function. For suitable wrappers of common R parallelization functions, see wrappers_parallel. Can be set to NULL if no parallelization is to be used.
parallel_arguments	An optional list of arguments to be passed to the optimization function.

Value

A list of S3 class gas_bootstrap with components:

data$y	The time series.
data$x	The exogenous variables.
model$distr	The conditional distribution.
model$param	The parametrization of the conditional distribution.
model$scaling	The scaling function.
model$regress	The specification of the regression and dynamic equation.
model$t	The length of the time series.
model$n	The dimension of the model.
model$m	The number of exogenous variables.
model$p	The score order.
model$q	The autoregressive order.
model$par_static	The static parameters.
model$par_link	The parameters with the logarithmic/logistic links.
model$par_init	The initial values of the time-varying parameters.
model$lik_skip	The number of skipped observations at the beginning of the time series or after NA values in the likelihood computation.
model$coef_fix_value	The values to which coefficients are fixed.
model$coef_fix_other	The multiples of the estimated coefficients, which are added to the fixed coefficients.
model$coef_fix_special	The predefined structures of coef_fix_value and coef_fix_other.
model$coef_bound_lower	The lower bounds on coefficients.
model$coef_bound_upper	The upper bounds on coefficients.
model$coef_est	The estimated coefficients.
bootstrap$method	The method used for bootstrapping.
bootstrap$coef_set	The bootstrapped sets of coefficients.
bootstrap$coef_mean	The mean of bootstrapped coefficients.
bootstrap$coef_vcov	The variance-covariance matrix of bootstrapped coefficients.
bootstrap$coef_sd	The standard deviation of bootstrapped coefficients.
bootstrap$coef_pval	The p-value of bootstrapped coefficients.
bootstrap$coef_quant	The quantiles of bootstrapped coefficients.

Note

Supported generic functions for S3 class gas_bootstrap include summary(), plot(), coef(), and vcov().

References

Creal, D., Koopman, S. J., and Lucas, A. (2013). Generalized Autoregressive Score Models with Applications. Journal of Applied Econometrics, 28(5), 777–795. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/jae.1279")}.

Harvey, A. C. (2013). Dynamic Models for Volatility and Heavy Tails: With Applications to Financial and Economic Time Series. Cambridge University Press. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1017/cbo9781139540933")}.

See Also

gas() wrappers_parallel

Examples

# Load the Daily Toilet Paper Sales dataset
data("toilet_paper_sales")
y <- toilet_paper_sales$quantity
x <- as.matrix(toilet_paper_sales[3:9])

# Estimate GAS model based on the negative binomial distribution
est_negbin <- gas(y = y, x = x, distr = "negbin", regress = "sep")
est_negbin

# Bootstrap the model (can be time-consuming for a larger number of samples)
boot_negbin <- gas_bootstrap(est_negbin, rep_boot = 10)
boot_negbin

# Plot boxplot of bootstrapped coefficients
plot(boot_negbin)

gasmodel documentation built on Aug. 19, 2025, 1:15 a.m.