compute_fars: Compute Factor Augmented Quantile Regressions and Stressed...
In FARS: Factor-Augmented Regression Scenarios

compute_fars

R Documentation

Compute Factor Augmented Quantile Regressions and Stressed Quantiles

Description

Performs quantile regressions of a dependent variable on MLDFM-extracted factors. Optionally generates quantile forecasts under stressed scenarios using hyperellipsoids.

Usage

compute_fars(
  dep_variable,
  factors,
  h = 1,
  edge = 0.05,
  scenario = NULL,
  min = TRUE,
  QTAU = 0.05
)

Arguments

`dep_variable`	A numeric vector representing the dependent variable (e.g., GDP growth, inflation).
`factors`	A matrix of factor estimates from a `mldfm` model.
`h`	Integer. Forecast horizon (in time steps) for the quantile regression. Default is `1`.
`edge`	Numeric. Trimming amount applied to the outermost quantiles (default `0.05`).
`scenario`	Optional list of matrices representing a stressed scenario, as returned by `create_scenario()`.
`min`	Logical. If `TRUE` (default), implement a stepwise minimization. If `FALSE`, implement a stepwise maximization.
`QTAU`	Numeric. Quantile level (default `0.05`) used to compute stressed factors via `compute_stressed_factors()`. Only used if `scenario` is provided.

Value

A list containing:

Quantiles: Matrix of forecasted quantiles (rows = time, cols = quantile levels).
Strssed_Quantiles: Matrix of stressed scenario quantiles (same format), returned only if scenario is provided.
Coeff: Matrix of quantile regression coefficients for each quantile.
Std. Error: Matrix of Std. Error for each regression coefficient.
Pvalue: Matrix of p-values for each regression coefficient.
QTAU: The quantile level used to compute stressed factors (if applicable).
Stressed_Factors: Matrix of selected stressed factors (only if scenario is provided and QTAU is set).

Examples


dep_variable <- rnorm(100)  # A numeric vector
data <- matrix(rnorm(100*300), nrow = 100, ncol = 300)
block_ind <- c(150, 300)  # Defines 2 blocks
global = 1
local <- c(1, 1)   
mldfm_result <- mldfm(data, blocks = 2, block_ind = block_ind, global = global , local = local)
fars_result <- compute_fars(dep_variable, mldfm_result$Factors, h = 1, edge = 0.05)

FARS documentation built on Aug. 8, 2025, 7:33 p.m.