Skip to contents

Reduced-Rank Approach (RRA) factor extraction

Source: R/rra_est.R
rra_est.Rd

Implements the two-step GMM estimator of He, Huang, Li, and Zhou (2023). Factor proxies X are rotated to maximise explanatory power for the target return matrix target, using diagonal GMM weighting matrices.

Usage

rra_est(target, X, nfac, compute_stat = FALSE)

Arguments

target

Numeric matrix (T x N) of target variables (e.g., asset returns). A vector is coerced to a T x 1 matrix.

X

Numeric matrix or data frame (T x L) of factor proxies.

nfac

Positive integer; number of RRA factors to extract.

compute_stat

Logical; if TRUE, compute the GMM J-test statistic for overidentifying restrictions. Returned as NULL when FALSE (default) or when degrees of freedom <= 0.

Value

An object of class "sdim_fit".

References

He, J., Huang, J., Li, F., and Zhou, G. (2023). Shrinking Factor Dimension: A Reduced-Rank Approach. Management Science, 69(9). doi:10.1287/mnsc.2022.4563

Examples

set.seed(1)
X <- matrix(rnorm(100 * 8), 100, 8)
Y <- matrix(rnorm(100 * 5), 100, 5)
fit <- rra_est(target = Y, X = X, nfac = 3)
print(fit)
#> <sdim_fit [rra]>
#>  Observations : 100 
#>  Predictors   : 8 
#>  Factors      : 3