FARS0.7.1 package

Factor-Augmented Regression Scenarios

apply_identifications

Apply Identification Constraints to Factors and Loadings

beta_ols

Efficient OLS Estimation

build_factor_structure

Build Factor Structure for Multi-level Dynamic Factor Model

canonical_correlation_analysis

Canonical Correlation Analysis for MLDFM

coef.fars

Coefficients for fars Object

compute_density

Compute Skew-t Densities from Quantiles

compute_fars

Compute Factor Augmented Quantile Regressions

compute_fpr_gamma

Compute Adaptive Threshold Cross-Sectional Robust Gamma (FPR Gamma)

compute_initial_factors

Compute Initial Factors for Multi-Level Dynamic Factor Model

compute_loadings

Compute Loadings

compute_optimal_delta

Compute Optimal Delta for FPR Gamma Computation

compute_stressed_factors

Compute Stressed Factors

compute_subsample

Compute Subsample of Data by Block

correct_outliers

Correct Dataset Outliers

create_scenario

Create Stressed Scenarios

factors.mldfm

Extract Estimated Factors from a mldfm Object

factors

Generic Function to Extract Estimated Factors

fitted.fars

Fitted Values for fars Object

fitted.mldfm

Extract Fitted Values from a mldfm Object

get_distribution.fars_density

Extract Distribution from a fars_density Object

get_distribution

Generic Function to Extract Distribution

get_ellipsoids.fars_scenario

Get Ellipsoids from a fars_scenario Object.

get_ellipsoids

Generic Function to Extract Ellipsoids

get_level_factors

Extract Factors at a Given Hierarchical Level

get_mldfm_list.mldfm_subsample

Extract List of MLDFMs from a mldfm_subsample Object

get_mldfm_list

Generic Function to Extract List of MLDFMs

get_mldfm_model.mldfm_subsample

Extract a Specific mldfm Object from a mldfm_subsample Object

get_mldfm_model

Generic Function to Extract a Specific mldfm Object

get_quantile_levels.fars

Extract Quantile Levels from a fars Object

get_quantile_levels

Generic Function to Extract Quantile Levels

get_rq_model.fars

Extract a Specific rq Object from a fars Object

get_rq_model

Generic Function to Extract a Specific rq Object

get_sigma_list

Generic Function to Get Sigma List

l_density

Compute Skew-t Densities from Quantiles (Linear Optimization)

loadings.mldfm

Extract Factor Loadings from a mldfm Object

loadings

Generic Function to Extract Factor Loadings

logLik.fars

Log-Likelihoods for fars Object

mldfm_subsampling

Subsampling Procedure for MLDFM Estimation

mldfm

Multi-Level Dynamic Factor Model (MLDFM)

multiple_blocks

Multi-level Dynamic Factor Model - Multiple Blocks (MLDFM)

nl_density

Compute Skew-t Densities from Quantiles (Non-Linear Optimization)

orthogonalize_factors

Orthogonalize Factors

plot_factors.mldfm

Plot Factors from mldfm Object

plot_loadings.mldfm

Plot Loadings from mldfm Object

plot_residuals.mldfm

Plot Residuals from mldfm Object

plot.fars_density

Plot Method for fars_density Object

plot.fars_scenario

Plot Method for fars_scenario Object

plot.fars

Plot Method for fars Object

plot.mldfm_subsample

Plot Method for mldfm_subsample Object

plot.mldfm

Plot Method for MLDFM object

predict.fars

Predict Method for fars Object

print.fars_density

Print Method for fars_density Object

print.fars_scenario

Print Method for fars_scenario Object

print.fars

Print Method for fars Object

print.mldfm_subsample

Print Method for mldfm_subsample Object

print.mldfm

Print Method for mldfm Object

quantile_risk

Extract Conditional Quantile from fars_density Object

residuals.fars

Residuals for fars Object

residuals.mldfm

Extract Residuals from a mldfm Object

single_block

Multi-Level Dynamic Factor Model - Single Block (DFM)

summary.fars_density

Summary Method for fars_density Object

summary.fars_scenario

Summary Method for fars_scenario Object

summary.fars

Summary Method for fars Object

summary.mldfm_subsample

Summary Method for mldfm_subsample Object

summary.mldfm

Summary Method for mldfm Object

update_factor_list

Update Factor List

Provides a comprehensive framework in R for modeling and forecasting economic scenarios based on multi-level dynamic factor model. The package enables users to: (i) extract global and group-specific factors using a flexible multi-level factor structure; (ii) compute asymptotically valid confidence regions for the estimated factors, accounting for uncertainty in the factor loadings; (iii) obtain estimates of the parameters of the factor-augmented quantile regressions together with their standard deviations; (iv) recover full predictive conditional densities from estimated quantiles; (v) obtain risk measures based on extreme quantiles of the conditional densities; (vi) estimate the conditional density and the corresponding extreme quantiles when the factors are stressed.