run_casestudy: Run the case study in KLTG (2020), or a smaller version...

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/header_replication.R

Description

Run the case study in KLTG (2020), or a smaller version thereof

Usage

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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 (2020) 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 (2020): ‘Predictive inference based on Markov chain Monte Carlo output’, International Statistical Review, forthcoming. 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

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

## End(Not run)

scoringRules documentation built on Oct. 23, 2020, 8:19 p.m.