run_casestudy: Run the case study in KLTG (2021), or a smaller version...

In scoringRules: Scoring Rules for Parametric and Simulated Distribution Forecasts

Run the case study in KLTG (2021), or a smaller version thereof

Description

Run the case study in KLTG (2021), or a smaller version thereof

Usage

run_casestudy(
  data_df,
  burnin_size = 5000,
  max_mcmc_sample_size = 5000,
  nr_of_chains = 3,
  first_vint = "1996Q2",
  last_vint = "2014Q3",
  forecast_horizon = 1,
  random_seed = 816
)

Arguments

data_df	data frame in the same format as the gdp data set in this package.
burnin_size	length of the burn-in period used for each forecast.
max_mcmc_sample_size	maximal number of MCMC draws to consider (integer, must equal either 1000, 5000, 10000, 20000 or 40000). Defaults to 5000.
nr_of_chains	number of parallel MCMC for each forecast date (integer, defaults to 3).
first_vint, last_vint	first and last data vintage (= time point at which forecasts are made). Default to "19962Q2" and "2014Q3", respectively.
forecast_horizon	forecast horizon to be analyzed (integer, defaults to 1).
random_seed	seed for random numbers used during the MCMC sampling process. Defaults to 816.

Details

The full results in Section 5 of KLTG (2021) are based on the following setup: burnin_size = 10000, max_mcmc_sample_size = 50000, nr_of_chains = 16, data_df = gdp, first_vint = "1996Q2", last_vint = "2014Q3", and forecast_horizon = 1. Since running this full configuration is very time consuming, the default setup offers the possibility to run a small-scale study which reproduces the qualitative outcomes of the analysis. Running the small-scale study implied by the defaults of run_study as well as the GDP data (data_df = gdp) takes about 40 minutes on an Intel i7 processor.

Value

Object of class "casestudy", containing the results of the analysis. This object can be passed to plot for plotting, see the documentation for plot.casestudy.

Author(s)

Fabian Krueger

References

Krueger, F., Lerch, S., Thorarinsdottir, T.L. and T. Gneiting (2021): ‘Predictive inference based on Markov chain Monte Carlo output’, International Statistical Review 89, 274-301. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/insr.12405")}

See Also

plot.casestudy produces a summary plot of the results generated by run_casestudy run_casestudy uses ar_ms to fit a Bayesian Markov Switching model, recursively for several time periods.

Examples

## Not run: 
data(gdp)
cs <- run_casestudy(data_df = gdp, last_vint = "1999Q4")
plot(cs)

## End(Not run)

scoringRules documentation built on Sept. 18, 2024, 5:09 p.m.