bpvars (version 1.0)

forecast_poos_recursively: Bayesian recursive pseudo-out-of-sample forecasting

Description

Performs the recursive pseudo-out-of-sample forecasting exercise using expanding window samples.

Usage

forecast_poos_recursively(model_spec, poos_spec, show_progress = TRUE)

Value

An object of class ForecastsPOOS containing the outcome of Bayesian recursive pseudo-out-of-sample forecasting exercise using expanding window samples. The object is a list with forecasting_sample elements, where

forecasting_sample is equal to the sample size less the maximum of

horizons and the training_sample plus one. Each element of the list is an object of class ForecastsPANEL containing the forecasts for each country, see forecast.PosteriorBVARPANEL.

Arguments

model_spec

an object generated using one of the specify_* functions containing model specification.

poos_spec

an object of class POOSForecastSetup containing specification of the recursive pseudo-out-of-sample forecasting exercise using expanding window samples.

show_progress

a logical value, if TRUE the estimation progress bar is visible

Author

Tomasz Woźniak

See Also

forecast.PosteriorBVARPANEL, specify_bvarPANEL, specify_poosf_exercise, estimate.BVARPANEL

Examples

spec = specify_bvarPANEL$new(ilo_dynamic_panel[1:5])   # specify the model
poos = specify_poosf_exercise$new(                # specify the forecasting exercise
         spec, 
         S = 5,                                   # use at least S = 5000
         S_burn = 2,                              # use at least S_burn = 1000
         horizons = 1,
         training_sample = 30
       )   
fore = forecast_poos_recursively(spec, poos)      # execute the forecasting exercise