sampleBTF_reg_backfit: (Backfitting) Sampler for first or second order random walk...

In drkowal/dsp: Dynamic Shrinkage Processes

(Backfitting) Sampler for first or second order random walk (RW) Gaussian dynamic linear model (DLM)

Description

Compute one draw of the T x p state variable beta in a DLM using back-band substitution methods. This model is equivalent to the Bayesian trend filtering (BTF) model applied to p dynamic regression coefficients corresponding to the design matrix X, assuming appropriate (shrinkage/sparsity) priors for the evolution errors. The sampler here uses a backfitting method that draws each predictor j=1,...,p conditional on the other predictors (and coefficients), which leads to a faster O(Tp) algorithm. However, the MCMC may be less efficient.

Usage

sampleBTF_reg_backfit(y, X, beta, obs_sigma_t2, evol_sigma_t2, D = 1)

Arguments

y	the T x 1 vector of time series observations
X	the T x p matrix of time series predictors
beta	the T x p matrix of previous dynamic regression coefficients
obs_sigma_t2	the T x 1 vector of observation error variances
evol_sigma_t2	the T x p matrix of evolution error variances
D	the degree of differencing (one or two)

Value

T x p matrix of simulated dynamic regression coefficients beta

Note

Missing entries (NAs) are not permitted in y. Imputation schemes are available.

drkowal/dsp documentation built on July 19, 2023, 11:42 a.m.