gas_filter: Filter GAS Model

In gasmodel: Generalized Autoregressive Score Models

Filter GAS Model

Description

A function for obtaining filtered time-varying parameters of generalized autoregressive score (GAS) models of Creal et al. (2013) and Harvey (2013). It captures parameter uncertainty and can also be used for forecasting. Method "simulated_coefs" computes a path of time-varying parameters for each simulated coefficient set under assumption of asymptotic normality with given variance-covariance matrix (see Blasques et al., 2016). Method "given_coefs" computes a path of time-varying parameters for each supplied coefficient set. Instead of supplying arguments about the model, the function can be applied to the gas object obtained by the gas() function.

Usage

gas_filter(
  gas_object = NULL,
  method = "simulated_coefs",
  coef_set = NULL,
  rep_gen = 1000L,
  t_ahead = 0L,
  x_ahead = NULL,
  rep_ahead = 1000L,
  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,
  coef_fix_value = NULL,
  coef_fix_other = NULL,
  coef_fix_special = NULL,
  coef_bound_lower = NULL,
  coef_bound_upper = NULL,
  coef_est = NULL,
  coef_vcov = NULL
)

Arguments

gas_object	An optional GAS estimate, i.e. a list of S3 class gas returned by function gas().
method	A method used for parameter uncertainty. Supported methods are "given_coefs" and "simulated_coefs".
coef_set	A numeric matrix of coefficient sets in rows for method = "given_coefs". Can be generated for example by gas_bootstrap().
rep_gen	A number of generated coefficient sets for method = "simulated_coefs".
t_ahead	A number of observations to forecast.
x_ahead	Out-of-sample exogenous variables. For a single variable common for all time-varying parameters, a numeric vector. For multiple variables common for all time-varying parameters, a numeric matrix with observations in rows. For individual variables for each time-varying parameter, a list of numeric vectors or matrices in the above form. The number of observation must be equal to t_ahead.
rep_ahead	A number of simulation repetitions for forecasting when t_ahead > 0.
quant	A numeric vector of probabilities determining quantiles.
y, x, distr, param, scaling, regress, p, q, par_static, par_link, par_init, coef_fix_value, coef_fix_other, coef_fix_special, coef_bound_lower, coef_bound_upper, coef_est, coef_vcov	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.

Value

A list of S3 class gas_filter with components:

data$y	The time series.
data$x	The exogenous variables.
data$x_ahead	The out-of-sample exogenous variables. Only when t_ahead > 0.
model$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$t_ahead	The length of the out-of-sample time series. Only when t_ahead > 0.
model$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$coef_fix_value	The values to which coefficients are fixed.
model$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_set	The coefficient sets.
filter$method	The method used for parameter uncertainty.
filter$par_tv_mean	The mean of the time-varying parameters.
filter$par_tv_sd	The standard deviation of the time-varying parameters.
filter$par_tv_quant	The quantiles of the time-varying parameters.
filter$score_tv_mean	The mean of the scores.
filter$score_tv_sd	The standard deviation of the scores.
filter$score_tv_quant	The quantiles of the scores.
filter$y_ahead_mean	The mean of the forecasted time series. Only when t_ahead > 0.
filter$y_ahead_sd	The standard deviation of the forecasted time series. Only when t_ahead > 0.
filter$y_ahead_quant	The quantiles of the forecasted time series. Only when t_ahead > 0.
filter$par_tv_ahead_mean	The mean of the forecasted time-varying parameters. Only when t_ahead > 0.
filter$par_tv_ahead_sd	The standard deviation of the forecasted time-varying parameters. Only when t_ahead > 0.
filter$par_tv_ahead_quant	The quantiles of the forecasted time-varying parameters. Only when t_ahead > 0.
filter$score_tv_ahead_mean	The mean of the forecasted scores. Only when t_ahead > 0.
filter$score_tv_ahead_sd	The standard deviation of the forecasted scores. Only when t_ahead > 0.
filter$score_tv_ahead_quant	The quantiles of the forecasted scores. Only when t_ahead > 0.

Note

Supported generic functions for S3 class gas_filter include summary() ans plot().

References

Blasques, F., Koopman, S. J., Łasak, K., and Lucas, A. (2016). In-Sample Confidence Bands and Out-of-Sample Forecast Bands for Time-Varying Parameters in Observation-Driven Models. International Journal of Forecasting, 32(3), 875–887. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.ijforecast.2015.11.018")}.

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()

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

# Filter the time-varying parameters by the "simulated_coefs" method
flt_negbin <- gas_filter(est_negbin, rep_gen = 100)
flt_negbin

# Plot the time-varying parameters with confidence bands
plot(flt_negbin)

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