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Florian, Huber ; Gary, Koop ; Niko, Hauzenberger ; Florian, Huber ; Niko, Hauzenberger ; Gary, Koop. (2023) Dynamic Shrinkage Priors for Large Time-varying Parameter Regressions using Scalable Markov Chain Monte Carlo Methods.
In: Papers. RePEc:arx:papers:2005.03906.
Full description at Econpapers
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Finally, in the case that λt evolves according to an AR(1) process with Gaussian shocks, we use precisely the same algorithm as Kastner and Frühwirth-Schnatter (2014) for simulating and ÃÂ. In the case that we use Z-distributed shocks, the algorithm proposed in Kowal et al. (2019) is adopted. This implies that we use Polya-Gamma (PG) auxiliary random variables to approximate the Z-distribution using a scale-mixture of Gaussians. Essentially, the main implication is that conditional on the T PG random variates, the parameters of the state evolution equation can be estimated similarly to the Gaussian case after normalizing everything by rendering the AR(1) conditionally homoscedastic. For more details, see Kowal et al. (2019).
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