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Fast Interactive Fixed Effects (IFE) for Generalized Synthetic Control

Source: R/RcppExports.R
gsc_ife_cpp.Rd

Implements Xu (2017) IFE model with optional covariate adjustment. When X_co has p > 0 slices, runs an EM loop alternating between: E-step: truncated SVD of Y_tilde = Y_co - X_co * beta M-step: panel OLS to update beta given current factors When X_co has 0 slices (default), falls back to the plain 3-step estimator.

Usage

gsc_ife_cpp(Y_co, Y_tr_pre, r, X_co, X_tr_pre, max_iter = 50L, tol = 1e-06)

Arguments

Y_co

Control units outcome matrix (T x N_co)

Y_tr_pre

Treated units pre-treatment outcomes (T_pre x N_tr)

r

Number of latent factors (must be <= min(T, N_co))

X_co

Time-varying covariate cube (T x N_co x p). Pass an empty cube (0 slices) for the covariate-free estimator.

X_tr_pre

Time-varying covariate cube for treated units in the pre-treatment window (T_pre x N_tr x p). Required for correct Step 2 loading estimation per Xu (2017): lambda_hat is estimated from Y_tr_pre - X_tr_pre * beta (covariate- demeaned). Pass an empty cube (0 slices) to skip demeaning (backward-compatible, but biased when beta != 0).

max_iter

Maximum EM iterations (default 50)

tol

Convergence tolerance on relative beta change (default 1e-6)

Value

A list with components:

  • F: estimated time factors (T x r).

  • L_co: control-unit factor loadings (N_co x r).

  • L_tr: treated-unit factor loadings (N_tr x r).

  • Y_tr_hat: estimated treated-unit counterfactual outcomes (T x N_tr).

  • singular_values: singular values from the final truncated SVD.

  • beta: estimated covariate coefficients (p x 1), empty when no covariates are supplied.