## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
    collapse = TRUE,
    comment = "#>"
)

## ----setup--------------------------------------------------------------------
library(koma)

## -----------------------------------------------------------------------------
equations <- "consumption ~ gdp + consumption.L(1) + interest_rate,
investment ~ gdp + investment.L(1) + interest_rate,
exports ~ world_gdp + exchange_rate + exports.L(1),
imports ~ gdp + exchange_rate + imports.L(1),
gdp == 0.55*consumption + 0.20*investment + 0.30*exports - 0.05*imports"

exogenous_variables <- c("interest_rate", "world_gdp", "exchange_rate")

## -----------------------------------------------------------------------------
sys_eq <- system_of_equations(
    equations = equations,
    exogenous_variables = exogenous_variables
)

print(sys_eq)

## -----------------------------------------------------------------------------
dates <- list(
    estimation = list(start = c(1996, 1), end = c(2019, 4)),
    forecast = list(start = c(2023, 1), end = c(2023, 4))
)

## -----------------------------------------------------------------------------
data("small_open_economy")
series <- unique(c(sys_eq$endogenous_variables, sys_eq$exogenous_variables))
ts_data <- small_open_economy[series]

## ----eval=FALSE---------------------------------------------------------------
# estimates <- estimate(ts_data, sys_eq, dates)
# #> ! The following series are plain <ts> objects, not <koma_ts>: "consumption",
# #>   "investment", "exports", "imports", "gdp", "interest_rate", "world_gdp",
# #>   and "exchange_rate".
# #> i They are assumed to already be in rates, the form the model estimates on,
# #>   and are converted to <koma_ts> with `series_type = "rate"`, `method =
# #>   "none"`. The values are used as-is; no rate/level transformation is
# #>   applied.
# #> i To convert a series from levels (e.g. a percentage or diff_log growth
# #>   rate), wrap it first with `ets()` or `as_ets()`. See
# #>   `vignette("koma-extended-timeseries")` for details.

## -----------------------------------------------------------------------------
ts_data <- lapply(ts_data, function(x) {
    as_ets(x, series_type = "level", method = "diff_log")
})
ts_data$interest_rate <- as_ets(
    ts_data$interest_rate,
    series_type = "rate",
    method = "none"
)

## -----------------------------------------------------------------------------
estimates <- estimate(
    ts_data,
    sys_eq,
    dates
)

print(estimates)
summary(estimates)

## -----------------------------------------------------------------------------
estimates$ts_data[sys_eq$endogenous_variables] <-
    lapply(sys_eq$endogenous_variables, function(x) {
        stats::window(estimates$ts_data[[x]], end = c(2022, 4))
    })

## -----------------------------------------------------------------------------
forecasts <- forecast(estimates, dates)
print(forecasts)

rate(forecasts$mean$gdp)
level(forecasts$mean$gdp)

## -----------------------------------------------------------------------------
summary(forecasts)
summary(forecasts, variables = "gdp", horizon = 2)

## ----eval=FALSE---------------------------------------------------------------
# if (requireNamespace("plotly", quietly = TRUE)) {
#     plot(forecasts, variables = c("gdp", "consumption"))
# }