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Time-Varying Structural Vector Autoregressions and Monetary Policy: a Corrigendum


Abstract: This note corrects a mistake in the estimation algorithm of the time-varying structural vector autoregression model of Primiceri (2005) and shows how to correctly apply the procedure of Kim, Shephard, and Chib (1998) to the estimation of VAR, DSGE, factor, and unobserved components models with stochastic volatility. Relative to Primiceri (2005), the main difference in the new algorithm is the ordering of the various Markov Chain Monte Carlo steps, with each individual step remaining the same.

Keywords: Bayesian methods; time-varying volatility;

JEL Classification: C11; C15;

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Provider: Federal Reserve Bank of New York

Part of Series: Staff Reports

Publication Date: 2014-10-01

Number: 619

Pages: 34 pages