Recursive Bayesian Regression | A Self-Contained Guide for Section 5.4 of Hansen & Sargent
The VAR Model We observe a vector time series $Z_1, Z_2, \ldots, Z_T \in \mathbb{R}^m$ following a VAR($\ell$): $$ Z_{t+1} = \tilde {\mathbb N} + D \begin{pmatrix} Z_t \\ Z_{t-1} \\ \vdots \\ Z_{t-\ell+1} \end{pmatrix} + F\,W_{t+1}, \qquad W_{t+1} \sim \mathcal{N}(0, I_k). \tag{1} $$Known: the signals $Z_1, \ldots, Z_T$. Unknown: the coefficient matrices $\tilde {\mathbb N}$, $D$, and the covariance $FF'$. Key idea. Treat $(\tilde {\mathbb N}, D)$ as hidden states that never change, so the problem becomes an Hidden Markdov Model (HMM) with trivial state dynamics $\beta_{t+1} = \beta_t$ and a linear observation equation. We then update beliefs about $\beta$ recursively as data arrives. ...