R: Forecasting and Filtering Distributions of the Latent States

ForecastingF {NGSSMEL}

R Documentation

Forecasting and Filtering Distributions of the Latent States

Description

The function ForecastingF computes the parameters the shape and scale (recursions) of the one-step-ahead forecast and filtering distributions of the latent states.

Usage

ForecastingF(StaPar,Yt,Xt=NULL,model="Poisson",a0=0.01,b0=0.01,distl="PRED")

Arguments

`StaPar`	is the static parameter vector
`Yt`	is the time series of interest
`Xt`	are the explanatory time series to be inserted in the model
`model`	is the chosen model for the observations
`a0`	is the shape parameter of the initial Gamma distribution
`b0`	is the scale parameter of the initial Gamma distribution
`distl`	is the latent states distribution to be returned

Details

If necessary, more details than the description above

Value

`att`	Description of 'att': 'att' is the shape parameter of the one-step-ahead forecast distribution of the states.
`btt`	Description of 'btt': 'btt' is the scale parameter of the one-step-ahead forecast distribution of the states.
`at`	Description of 'at': 'at' is the shape parameter of the filtering distribution of the states. It is necessary to specify this option in the argument 'distl'.
`bt`	Description of 'bt': 'bt' is the scale parameter of the filtering distribution of the states. It is necessary to specify this option in the argument 'distl'.

Author(s)

Thiago Rezende dos Santos

References

Gamerman, D., Santos, T. R., and Franco, G. C. (2013). A Non-Gaussian Family of State-Space Models with Exact Marginal Likelihood. Journal of Time Series Analysis, 34(6), 625-645.

Harvey, A. C., and Fernandes, C. (1989). Time series models for count or qualitative observations. Journal of Business and Economic Statistics, 7(4), 407-417.

Examples





# Forecasting and Filtering for the states
#  ForFunc(StaPar,Yt,Xt="NULL",model="Poisson",a0=0.01,b0=0.01)
Yt=c(1,2,1,4,3)
Par=c(0.9) #w
predpar=ForecastingF(Par,Yt,Xt=NULL,model="Poisson",a0=0.01,b0=0.01)
predpar
filpar=ForecastingF(Par,Yt,Xt=NULL,model="Poisson",
a0=0.01,b0=0.01,distl="FILTER")
filpar

[Package NGSSMEL version 1.0 Index]