tsPI (version 1.0.4)

avg_coverage_struct: Compute the average coverage of the prediction intervals computed by struct_pi and plug-in method

Description

Computes expected coverage probabilities of the prediction intervals of structural time series model. Note that for the plug-in method only standard deviations are assumed to be identical to their estimates, but the initial values for the states are still treated as diffuse. Because of this, plug-in method often performs relatively well in case of structural time series models compared to similar type of ARIMA models (local level and local linear trend models are closely related to ARIMA(0,1,1) and ARIMA(0,2,2) models), and in some cases even outperforms the importance sampling approach with uniform prior (see examples). This is not suprising, as local level and local linear trend models are closely related to ARIMA(0,1,1) and ARIMA(0,2,2) models, and the effect of uncertainty in MA components is not as significant as the uncertainty of AR components

Usage

avg_coverage_struct(
  type = c("level", "trend", "BSM"),
  sds,
  frequency = 1,
  n,
  n_ahead = 1,
  nsim2,
  nsim = 100,
  level = 0.95,
  prior = "uniform",
  return_all_coverages = FALSE,
  ...
)

Value

a list containing the coverage probabilities

Arguments

type

Type of model. See struct_pi.

sds

vector containing the standard deviations of the model (observation error, level, slope, and seasonal).

frequency

frequency of the series, needed for seasonal component.

n

length of the time series

n_ahead

length of the forecast horizon

nsim2

number of simulations used in computing the expected coverage

nsim

number of simulations used in importance sampling

level

desired coverage probability of the prediction intervals

prior

prior to be used in importance sampling.

return_all_coverages

return raw results i.e. coverages for each simulations. When FALSE (default), summary statistics are returned.

...

additional arguments to struct_pi.

See Also

struct_pi.

Examples

if (FALSE) {
set.seed(123)
# takes a while, notice se, increase nsim2 to get more accurate results
avg_coverage_struct(type = "level", sds = c(1, 0.1), n = 50, n_ahead = 10, nsim2 = 100)
avg_coverage_struct(type = "BSM", sds = c(1, 1, 0.1, 10),
 frequency = 4, n = 50, n_ahead = 10, nsim2 = 100)
}