ConfidenceBands: Build confidence bands for the filtered parameters

In GAS: Generalized Autoregressive Score Models

Build confidence bands for the filtered parameters

Description

Build confidence bands for the filtered parameters sampling the coefficients from the asymptotic distribution as in Blasques et al. (2016).

Usage

ConfidenceBands(object, B = 10000, probs = c(0.01,0.1,0.9,0.99), ...)

Arguments

object	An object of the class uGASFit or mGASFit
B	numeric Number of draws from the asymptotic distributions.
probs	numeric Quantiles to returns.
...	Additional arguments.

Details

This function implements the "In-Sample Simulation-Based Bands" Section 3.3 of Blasques et al. (2016).

Value

An object of the class array of dimension (T+1) x B x K, where T is the length of the time series, K is the number of parameters and B the number of draws. The first slice reports the estimated filtered parameters. The one step ahead prediction is also reported, this why T+1.

Author(s)

Leopoldo Catania

References

Blasques F, Koopman SJ, Lasak 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.2016.04.002")}.

Examples

## Not run: 

# show the information of all the supported distributions
library("GAS")

data("cpichg")

GASSpec = UniGASSpec(Dist = "std", ScalingType = "Identity",
                     GASPar = list(location = TRUE, scale = TRUE,
                                   shape = FALSE))

Fit = UniGASFit(GASSpec, cpichg)

Bands = ConfidenceBands(Fit)

## End(Not run)

GAS documentation built on Sept. 11, 2024, 8:13 p.m.