Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors

Andrea Carriero, Todd Clark and Massimiliano Marcellino

Journal of Econometrics, 2019, vol. 212, issue 1, 137-154

Abstract: Recent research has shown that a reliable vector autoregression (VAR) for forecasting and structural analysis of macroeconomic data requires a large set of variables and modeling time variation in their volatilities. Yet, there are no papers that provide a general solution for combining these features, due to computational complexity. Moreover, homoskedastic Bayesian VARs for large data sets so far restrict substantially the allowed prior distributions on the parameters. In this paper we propose a new Bayesian estimation procedure for (possibly very large) VARs featuring time-varying volatilities and general priors. We show that indeed empirically the new estimation procedure performs well in applications to both structural analysis and out-of-sample forecasting.

Keywords: Big data; Forecasting; Structural VAR (search for similar items in EconPapers)
JEL-codes: C11 C13 C33 C53 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (146)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:212:y:2019:i:1:p:137-154

DOI: 10.1016/j.jeconom.2019.04.024

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