data("gar_data")
test_params = list()
test_params$horizon_list = c(4,8)
test_params$quantile_vec = c(0.05,0.25,0.5,0.75,0.95)
gar_analisys = run_GaR_analysis(
partitions_list = list(
dom_macro = c("gdp", "ind_prod_israel"),
fin_cycle = c("credit", "house_price")
),
vars_df = gar_data %>%
mutate(date = as.yearqtr(date)) %>%
preprocess_df(
vars_to_yoy = c("gdp", "ind_prod_israel", "credit", "house_price")
),
target_var_name = "gdp",
horizon_list = test_params$horizon_list,
quantile_vec = test_params$quantile_vec
)
test_fit_skew_df = gar_analisys$fitted_df %>%
filter(date == as.yearqtr("2000 Q1")) %>%
fit_t_skew_to_gar_df()
test_smoothed = extract_smoothed_quantiles(test_fit_skew_df,
gar_analisys$fitted_df %>%
filter(date %in% c(as.yearqtr("2000 Q1"),
as.yearqtr("2000 Q2"))) %>%
rename(values = fitted_values))
test_that(
"smoothing returns smoothed values where available",
expect_equal(
test_smoothed %>%
filter(date == as.yearqtr("2000 Q1")) %>%
pull(values_smoothed),
test_smoothed %>%
filter(date == as.yearqtr("2000 Q1")) %>%
pull(value)
)
)
test_that(
"smoothing returns raw values where smoothed unavailable",
expect_equal(
test_smoothed %>%
filter(date == as.yearqtr("2000 Q2")) %>%
pull(values_raw),
test_smoothed %>%
filter(date == as.yearqtr("2000 Q2")) %>%
pull(value)
)
)
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