Provides posterior summary of country-specific Forecasts
Source:R/summary.R
summary.ForecastsPANEL.RdProvides posterior summary of the forecasts including their mean, standard deviations, as well as 5 and 95 percentiles.
Usage
# S3 method for class 'ForecastsPANEL'
summary(object, which_c, ...)Arguments
- object
an object of class
ForecastsPANELobtained using theforecast()function containing draws the predictive density.- which_c
a positive integer or a character string specifying the country for which the forecast should be plotted.
- ...
additional arguments affecting the summary produced.
Value
A list reporting the posterior mean, standard deviations, as well as 5 and 95 percentiles of the forecasts for each of the variables and forecast horizons.
Author
Tomasz Woźniak wozniak.tom@pm.me
Examples
# specify the model
specification = specify_bvarPANEL$new(
ilo_dynamic_panel,
exogenous = ilo_exogenous_variables)
burn_in = estimate(specification, 10) # run the burn-in
#> **************************************************|
#> bpvars: Forecasting with Bayesian Panel VARs |
#> **************************************************|
#> Progress of the MCMC simulation for 10 draws
#> Every draw is saved via MCMC thinning
#> Press Esc to interrupt the computations
#> **************************************************|
posterior = estimate(burn_in, 10) # estimate the model
#> **************************************************|
#> bpvars: Forecasting with Bayesian Panel VARs |
#> **************************************************|
#> Progress of the MCMC simulation for 10 draws
#> Every draw is saved via MCMC thinning
#> Press Esc to interrupt the computations
#> **************************************************|
# forecast 6 years ahead
predictive = forecast(
posterior,
6,
exogenous_forecast = ilo_exogenous_forecasts)
#> **************************************************|
#> bpvars: Forecasting with Bayesian Panel VARs |
#> **************************************************|
#> Progress of sampling 10 draws from
#> the predictive density for 189 countries
#> Press Esc to interrupt the computations
#> **************************************************|
summary(predictive, which_c = "POL")
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#> Posterior summary of forecasts |
#> **************************************************|
#> $variable1
#> mean sd 5% quantile 95% quantile
#> 1 27.20572 0.01713435 27.17839 27.22276
#> 2 27.23621 0.02471803 27.19926 27.26745
#> 3 27.26906 0.03790437 27.20663 27.31474
#> 4 27.30356 0.05125057 27.23776 27.37668
#> 5 27.33231 0.06035702 27.25292 27.41278
#> 6 27.35248 0.06526581 27.26065 27.43027
#>
#> $variable2
#> mean sd 5% quantile 95% quantile
#> 1 2.657395 1.296731 0.8322561 4.304127
#> 2 3.371238 2.385561 0.6838957 6.542516
#> 3 3.778699 3.424897 -1.0569508 8.345179
#> 4 2.991731 3.938224 -1.4595711 8.444515
#> 5 2.315362 4.819355 -3.6906717 9.105023
#> 6 2.071746 4.199494 -3.5932886 7.217697
#>
#> $variable3
#> mean sd 5% quantile 95% quantile
#> 1 57.13529 0.7046672 56.17041 58.00444
#> 2 56.85915 1.5330654 54.71749 58.62135
#> 3 56.77072 1.9419717 54.21948 59.63737
#> 4 57.41043 2.3975274 54.45045 60.23617
#> 5 57.77458 3.0246026 53.61735 61.00221
#> 6 58.08162 2.8383532 54.34007 61.65279
#>
#> $variable4
#> mean sd 5% quantile 95% quantile
#> 1 58.75354 0.4534078 58.13107 59.38133
#> 2 58.91937 0.6032468 57.87655 59.42175
#> 3 59.08754 0.6026798 58.19990 59.82331
#> 4 59.31735 0.6232068 58.41958 60.01503
#> 5 59.32579 0.8421962 58.04522 60.39250
#> 6 59.52576 1.1836941 57.69693 61.04103
#>
# workflow with the pipe |>
############################################################
ilo_dynamic_panel |>
specify_bvarPANEL$new() |>
estimate(S = 10) |>
estimate(S = 20) |>
forecast(horizon = 2) |>
summary(which_c = "POL")
#> **************************************************|
#> bpvars: Forecasting with Bayesian Panel VARs |
#> **************************************************|
#> Progress of the MCMC simulation for 10 draws
#> Every draw is saved via MCMC thinning
#> Press Esc to interrupt the computations
#> **************************************************|
#> **************************************************|
#> bpvars: Forecasting with Bayesian Panel VARs |
#> **************************************************|
#> Progress of the MCMC simulation for 20 draws
#> Every draw is saved via MCMC thinning
#> Press Esc to interrupt the computations
#> **************************************************|
#> **************************************************|
#> bpvars: Forecasting with Bayesian Panel VARs |
#> **************************************************|
#> Progress of sampling 20 draws from
#> the predictive density for 189 countries
#> Press Esc to interrupt the computations
#> **************************************************|
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#> Posterior summary of forecasts |
#> **************************************************|
#> $variable1
#> mean sd 5% quantile 95% quantile
#> 1 27.21471 0.02654923 27.17621 27.25369
#> 2 27.25864 0.04002221 27.20596 27.32313
#>
#> $variable2
#> mean sd 5% quantile 95% quantile
#> 1 2.912610 1.793348 0.01351445 5.542122
#> 2 2.599017 2.808424 -1.55231303 6.862643
#>
#> $variable3
#> mean sd 5% quantile 95% quantile
#> 1 56.93790 0.866412 55.57193 58.15785
#> 2 57.04864 1.471670 55.23364 59.03791
#>
#> $variable4
#> mean sd 5% quantile 95% quantile
#> 1 58.69016 0.4247980 58.09202 59.40445
#> 2 58.64120 0.5487356 57.97211 59.63082
#>
# conditional forecasting 6 years ahead conditioning on
# provided future values for the Gross Domestic Product
# growth rate
############################################################
specification = specify_bvarPANEL$new(ilo_dynamic_panel) # specify the model
burn_in = estimate(specification, 10) # run the burn-in
#> **************************************************|
#> bpvars: Forecasting with Bayesian Panel VARs |
#> **************************************************|
#> Progress of the MCMC simulation for 10 draws
#> Every draw is saved via MCMC thinning
#> Press Esc to interrupt the computations
#> **************************************************|
posterior = estimate(burn_in, 10) # estimate the model
#> **************************************************|
#> bpvars: Forecasting with Bayesian Panel VARs |
#> **************************************************|
#> Progress of the MCMC simulation for 10 draws
#> Every draw is saved via MCMC thinning
#> Press Esc to interrupt the computations
#> **************************************************|
# forecast 6 years ahead
predictive = forecast(posterior, 6, conditional_forecast = ilo_conditional_forecasts)
#> **************************************************|
#> bpvars: Forecasting with Bayesian Panel VARs |
#> **************************************************|
#> Progress of sampling 10 draws from
#> the predictive density for 189 countries
#> Press Esc to interrupt the computations
#> **************************************************|
summary(predictive, which_c = "POL")
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#> Posterior summary of forecasts |
#> **************************************************|
#> $variable1
#> mean sd 5% quantile 95% quantile
#> 1 27.20126 0 27.20126 27.20126
#> 2 27.23543 0 27.23543 27.23543
#> 3 27.26786 0 27.26786 27.26786
#> 4 27.29738 0 27.29738 27.29738
#> 5 27.32664 0 27.32664 27.32664
#> 6 27.35630 0 27.35630 27.35630
#>
#> $variable2
#> mean sd 5% quantile 95% quantile
#> 1 3.381242 1.697315 0.93017247 5.548099
#> 2 3.494060 3.018412 -0.07421561 7.755544
#> 3 3.431616 3.978301 -0.61876448 9.497034
#> 4 3.406686 4.385033 -1.25295177 9.741378
#> 5 3.233877 4.697363 -2.83257460 8.956343
#> 6 2.634449 5.042335 -3.80131918 8.387920
#>
#> $variable3
#> mean sd 5% quantile 95% quantile
#> 1 56.67397 0.8637966 55.59580 57.98038
#> 2 56.73316 1.6669955 54.17275 58.96951
#> 3 56.66879 2.3019324 52.98519 59.34358
#> 4 56.52904 2.7428799 52.40603 59.64295
#> 5 56.83278 2.8990082 52.51661 60.06281
#> 6 57.14279 3.0047746 53.47715 61.03857
#>
#> $variable4
#> mean sd 5% quantile 95% quantile
#> 1 58.69842 0.6033508 57.83911 59.46098
#> 2 58.87325 0.7829074 57.62750 59.69134
#> 3 58.79514 0.9327940 57.39489 59.83575
#> 4 58.62168 1.0188026 57.02006 59.94763
#> 5 58.83143 1.3528214 56.83283 60.36296
#> 6 58.83097 1.3459496 56.55677 60.12602
#>
# workflow with the pipe |>
############################################################
ilo_dynamic_panel |>
specify_bvarPANEL$new() |>
estimate(S = 10) |>
estimate(S = 20) |>
forecast(
horizon = 6,
conditional_forecast = ilo_conditional_forecasts
) |>
summary(which_c = "POL")
#> **************************************************|
#> bpvars: Forecasting with Bayesian Panel VARs |
#> **************************************************|
#> Progress of the MCMC simulation for 10 draws
#> Every draw is saved via MCMC thinning
#> Press Esc to interrupt the computations
#> **************************************************|
#> **************************************************|
#> bpvars: Forecasting with Bayesian Panel VARs |
#> **************************************************|
#> Progress of the MCMC simulation for 20 draws
#> Every draw is saved via MCMC thinning
#> Press Esc to interrupt the computations
#> **************************************************|
#> **************************************************|
#> bpvars: Forecasting with Bayesian Panel VARs |
#> **************************************************|
#> Progress of sampling 20 draws from
#> the predictive density for 189 countries
#> Press Esc to interrupt the computations
#> **************************************************|
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#> Posterior summary of forecasts |
#> **************************************************|
#> $variable1
#> mean sd 5% quantile 95% quantile
#> 1 27.20126 0 27.20126 27.20126
#> 2 27.23543 0 27.23543 27.23543
#> 3 27.26786 0 27.26786 27.26786
#> 4 27.29738 0 27.29738 27.29738
#> 5 27.32664 0 27.32664 27.32664
#> 6 27.35630 0 27.35630 27.35630
#>
#> $variable2
#> mean sd 5% quantile 95% quantile
#> 1 2.626172 1.511814 0.4110344 4.502724
#> 2 2.977185 2.410963 -2.2871937 6.046909
#> 3 2.729622 2.973580 -2.0413994 6.756203
#> 4 2.627761 4.121769 -3.5702656 7.347993
#> 5 2.635623 4.607727 -2.6762139 8.886457
#> 6 2.737346 4.990901 -3.5267335 8.468564
#>
#> $variable3
#> mean sd 5% quantile 95% quantile
#> 1 57.26719 0.7281697 56.03709 58.29942
#> 2 57.26437 1.1980792 55.67203 59.87396
#> 3 57.50356 1.5251371 55.37847 60.27276
#> 4 57.72876 2.1272563 55.13855 60.82074
#> 5 57.82116 2.4933866 54.34246 61.36448
#> 6 57.97642 2.7872370 54.44538 62.09246
#>
#> $variable4
#> mean sd 5% quantile 95% quantile
#> 1 58.88447 0.4399017 58.23702 59.50339
#> 2 59.12074 0.4756367 58.49965 59.82913
#> 3 59.24966 0.8035938 58.11454 60.39495
#> 4 59.44956 0.9388350 58.33534 60.78632
#> 5 59.56611 0.7763338 58.79451 60.41785
#> 6 59.80011 0.8984679 58.72104 61.06092
#>