Forecast the Simultaneous Equations Model (SEM)

This function produces forecasts for the SEM.

Usage

forecast(estimates, dates, ..., restrictions = NULL, point_forecast = NULL)

Arguments

estimates

A koma_estimate object (estimate) containing the estimates for the simultaneous equations model, as well as a list of time series and a koma_seq object (system_of_equations) that were used in the estimation.

dates

Key-value list for date ranges in various model operations.

...

Additional parameters.

restrictions

List of model constraints. Default is empty.

point_forecast

A list that contains the following elements:

active: Determines the type of forecast generated. If TRUE, a point forecast is created. If FALSE, a density forecast is returned. Default is TRUE.
central_tendency: A character string indicating which central tendency measure ("mean" or "median") to use for summary statistics. Default is "mean".

Value

An object of class koma_forecast.

An object of class koma_forecast is a list containing the following elements:

mean: Mean point forecasts as a list of time series of class koma_ts.
median: Median point forecasts as a list of time series of class koma_ts.
quantiles: A list of quantiles, where each element is named according to the quantile (e.g., "5", "50", "95"), and contains the forecasts for that quantile. This element is NULL if quantiles = FALSE.
ts_data: Time-series data set used in forecasting.
y_matrix: The Y matrix constructed from the balanced data up to the current quarter, used for forecasting.
x_matrix: The X matrix used for forecasting.

Details

The forecast function for SEM uses the estimates from the koma_estimate object to produce point forecasts or quantile forecasts based on the point_forecast parameter. If point_forecast$active is TRUE, only point forecasts are generated. If FALSE, quantile forecasts are generated and included in the quantiles list.

Use the summary function to print a summary of the forecast results. The plot function can be used to visualize the forecasts and prediction intervals.

Parallel

This function provides the option for parallel computing through the future::plan() function. For a detailed example on executing estimate in parallel, see the vignette: vignette("parallel"). For more details, see the future package documentation.