hdiVAR:Statistical Inference for Noisy Vector Autoregression
The model is high-dimensional vector autoregression with measurement error, also known as linear gaussian state-space
model. Provable sparse expectation-maximization algorithm is
provided for the estimation of transition matrix and noise
variances. Global and simultaneous testings are implemented for
transition matrix with false discovery rate control. For more
information, see the accompanying paper: Lyu, X., Kang, J., &
Li, L. (2023). "Statistical inference for high-dimensional
vector autoregression with measurement error", Statistica
Sinica.
