kalman_smoother_cpp: Kalman Filter and RTS Smoother (TASC)

In coresynth: Fast and Unified Synthetic Control Methods

Kalman Filter and RTS Smoother (TASC)

Description

Implements the Kalman filter (forward pass) and Rauch-Tung-Striebel smoother (backward pass) for the state-space model in Rho et al. (2026):

Usage

kalman_smoother_cpp(Y, W, A, C, Q, R, z0, P0)

Arguments

Y	Observed data matrix (N x T). Use NA for unobserved entries.
W	Observation / loading matrix (N x r)
A	State transition matrix (r x r). Pass diag(r) for random-walk dynamics.
C	State drift vector (r x 1)
Q	State noise covariance (r x r)
R	Observation noise covariance (N x N, diagonal in practice)
z0	Initial state mean (r x 1)
P0	Initial state covariance (r x r)

Details

State: z(t+1) = A z(t) + C + eta(t), eta(t) ~ N(0, Q) Observation: y_t = W z_t + eps_t, eps_t ~ N(0, R)

Observation rows with NA (treated post-intervention) are automatically dropped at each time step so only control-unit rows update the filter.

The P update uses the numerically stable Joseph form: P(t|t) = (I - K W_obs) P(t|t-1) (I - K W_obs)^T + K R_obs K^T

Value

A list with z_smooth, P_smooth, P_cross, z_pred, z_upd. P_cross is an r x r x (T-1) cube. Slice t (C++ 0-indexed, t=0,...,T-2) stores P(t+1, t | T) (0-indexed), i.e. P(t+2, t+1 | T) in 1-indexed Shumway-Stoffer notation. Formula: P(t+1|T) * J_t^T (eq. 6.68-6.69).

coresynth documentation built on Aug. 28, 2026, 1:06 a.m.