# kalmanUnivariate: Univariate filtering (sequential processing) for fast KFS In sparseDFM: Estimate Dynamic Factor Models with Sparse Loadings

 kalmanUnivariate R Documentation

## Univariate filtering (sequential processing) for fast KFS

### Description

Univariate treatment (sequential processing) of the multivariate Kalman filter and smoother equations for fast implementation. Refer to Koopman and Durbin (2000).

### Usage

kalmanUnivariate(X, a0_0, P0_0, A, Lambda, Sig_e, Sig_u)


### Arguments

 X n x p, numeric matrix of (stationary) time series a0_0 k x 1, initial state mean vector P0_0 k x k, initial state covariance matrix A k x k, state transition matrix Lambda p x k, measurement matrix Sig_e p x p, measurement equation residuals covariance matrix (diagonal) Sig_u k x k, state equation residuals covariance matrix

### Details

For full details of the univariate filtering approach, please refer to Mosley et al. (2023). Note that n is the number of observations, p is the number of time series, and k is the number of states.

### Value

logl log-likelihood of the innovations from the Kalman filter

at_t k \times n, filtered state mean vectors

Pt_t k \times k \times n, filtered state covariance matrices

at_n k \times n, smoothed state mean vectors

Pt_n k \times k \times n, smoothed state covariance matrices

Pt_tlag_n k \times k \times n, smoothed state covariance with lag

### References

Koopman, S. J., & Durbin, J. (2000). Fast filtering and smoothing for multivariate state space models. Journal of Time Series Analysis, 21(3), 281-296.

Mosley, L., Chan, TS., & Gibberd, A. (2023). sparseDFM: An R Package to Estimate Dynamic Factor Models with Sparse Loadings.

sparseDFM documentation built on March 31, 2023, 10:15 p.m.