Real-time conditional forecasts with Bayesian VARs: An application to New Zealand
Chris Bloor () and
Troy Matheson ()
No DP2009/02, Reserve Bank of New Zealand Discussion Paper Series from Reserve Bank of New Zealand
Abstract:
We develop a large Bayesian VAR (BVAR) model of the New Zealand economy that incorporates the conditional forecasting estimation techniques of Waggoner and Zha (1999). We examine the real-time forecasting performance as the size of the model increases using an unbalanced data panel. In a realtime out-of-sample forecasting exercise, we find that our BVAR methodology outperforms univariate and VAR benchmarks, and produces comparable forecast accuracy to the judgementally-adjusted forecasts produced internally at the Reserve Bank of New Zealand. We analyse forecast performance and find that, while there are trade offs across different variables, a 35 variable BVAR generally performs better than 8, 13, or 50 variable specifications for our dataset. Finally, we demonstrate techniques for imposing judgement and for forming a semi-structural interpretation of the BVAR forecasts.
JEL-codes: C11 C13 C53 (search for similar items in EconPapers)
Pages: 33p
Date: 2009-04
New Economics Papers: this item is included in nep-cba, nep-ecm and nep-for
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Citations: View citations in EconPapers (14)
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Journal Article: Real-time conditional forecasts with Bayesian VARs: An application to New Zealand (2011) 
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Persistent link: https://EconPapers.repec.org/RePEc:nzb:nzbdps:2009/02
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