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_targets.R
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_targets.R
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# _targets.R file
library(targets)
tt <- tar_target
library(future)
library(future.callr)
plan(callr)
# global packages
options(tidyverse.quiet = TRUE)
library(tidyverse)
library(rstan)
options(mc.cores = parallel::detectCores())
glu <- glue::glue
# importing source code for the paper
source("R/functions.R")
# Definition of the pipeline
list(
# Data generation targets -----
tt(full_sample,
gen_sample(begin_date = "1988-01-01",
end_date = "2018-01-01",
trunc_lvl = 0.01),
packages = c("RPostgres", "tidyverse")
),
# Running models --------------
tt(AQwo,
run_model(full_sample, modelname = "AQwo")
),
tt(Fi_AQwo,
run_model(full_sample, modelname = "Fi-AQwo")
),
tt(FrankelSun,
run_model(full_sample, modelname = "FrankelSun")
),
tt(Fi_FrankelSun,
run_model(full_sample, modelname = "Fi-FrankelSun")
),
tt(ModJones,
run_model(full_sample, modelname = "ModJones")
),
tt(Fi_ModJones,
run_model(full_sample, modelname = "Fi-ModJones")
),
tt(ModJonesROA,
run_model(full_sample, modelname = "ModJonesROA")
),
tt(Fi_ModJonesROA,
run_model(full_sample, modelname = "Fi-ModJonesROA")
),
tt(ModJonesAQwo,
run_model(full_sample, modelname = "ModJonesAQwo")
),
tt(Fi_ModJonesAQwo,
run_model(full_sample, modelname = "Fi-ModJonesAQwo")
),
tt(Collins,
run_model(full_sample, modelname = "Collins")
),
tt(Fi_Collins,
run_model(full_sample, modelname = "Fi-Collins")
),
tt(BallShiva,
run_model(full_sample, modelname = "BallShiva")
),
tt(Fi_BallShiva,
run_model(full_sample, modelname = "Fi-BallShiva")
),
tt(loo_AQwo,
compute_loo_diag(AQwo)
),
tt(loo_Fi_AQwo,
compute_loo_diag(Fi_AQwo)
),
tt(loo_FrankelSun,
compute_loo_diag(FrankelSun)
),
tt(loo_Fi_FrankelSun,
compute_loo_diag(Fi_FrankelSun)
),
tt(loo_ModJones,
compute_loo_diag(ModJones)
),
tt(loo_Fi_ModJones,
compute_loo_diag(Fi_ModJones)
),
tt(loo_ModJonesROA,
compute_loo_diag(ModJonesROA)
),
tt(loo_Fi_ModJonesROA,
compute_loo_diag(Fi_ModJonesROA)
),
tt(loo_ModJonesAQwo,
compute_loo_diag(ModJonesAQwo)
),
tt(loo_Fi_ModJonesAQwo,
compute_loo_diag(Fi_ModJonesAQwo)
),
tt(loo_Collins,
compute_loo_diag(Collins)
),
tt(loo_Fi_Collins,
compute_loo_diag(Fi_Collins)
),
tt(loo_BallShiva,
compute_loo_diag(BallShiva)
),
tt(loo_Fi_BallShiva,
compute_loo_diag(Fi_BallShiva)
),
tt(loo_weights,
gen_stacking_weights(list(
"AQwo" = loo_AQwo,
"Fi-AQwo" = loo_Fi_AQwo,
"FrankelSun" = loo_FrankelSun,
"Fi-FrankelSun" = loo_Fi_FrankelSun,
"ModJones" = loo_ModJones,
"Fi-ModJones" = loo_Fi_ModJones,
"ModJonesROA" = loo_ModJonesROA,
"Fi-ModJonesROA" = loo_Fi_ModJonesROA,
"ModJonesAQwo" = loo_ModJonesAQwo,
"Fi-ModJonesAQwo" = loo_Fi_ModJonesAQwo,
"Collins" = loo_Collins,
"Fi-Collins" = loo_Fi_Collins,
"BallShiva" = loo_BallShiva,
"Fi-BallShiva" = loo_Fi_BallShiva
)),
packages = c("loo")
),
tt(averaged_bayes_oos_preds,
gen_averaged_oos_preds(weight_table = loo_weights, full_sample)
),
tt(save_ndas,
write_output(bayes_nda = averaged_bayes_oos_preds,
fit_sample = full_sample,
out_path = "out/bayes-ndas.csv")
)
)