Large Vector Autoregressions with Stochastic Volatility and Flexible Priors
Andrea Carriero () and
No 1617, Working Papers (Old Series) from Federal Reserve Bank of Cleveland
Recent research has shown that a reliable vector autoregressive model (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 jointly allowing for stochastic volatilities and large datasets, due to computational complexity. Moreover, homoskedastic VAR models for large datasets 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. This is important both for reduced form applications, such as forecasting, and for more structural applications, such as computing response functions to structural shocks. We show that indeed empirically the new estimation procedure performs very well for both tasks.
Keywords: forecasting; models; structural shocks (search for similar items in EconPapers)
JEL-codes: C11 C13 C33 C53 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for, nep-ger and nep-ore
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