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- We use the algorithm of Kastner and Frühwirth-Schnatter (2014) to take draws of ht. A.2. Sampling the Time-Invariant Regression Coefficients Most of the conditional posterior distributions take a simple and well-known form. Here we briefly summarize these and provide some information on the relevant literature. The time-invariant coefficients α follow a K-dimensional multivariate Gaussian posterior given by α|• ∼ N(α, V α), V α = X̃0 X̃ + D−1 α −1 , α = V αX̃ŷ, with X̃ = L−1 X, ŷ = L−1 (y − W β) and Dα = Äα diag(È2 1, . . . , È2 K) denoting a K × K-dimensional prior variance-covariance matrix with Èj (j = 1, . . . , K) and √ Äα following a half-Cauchy distribution, respectively.
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