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.
Download Info
To download:
If you experience problems downloading a file, check if you have the
proper application to
view it first. Information about this may be contained
in the File-Format links below. In case of further problems read
the IDEAS help
page. Note that these files are not on the IDEAS
site. Please be patient as the files may be large.
For technical questions regarding this item, or to correct its listing, contact: (Reserve Bank of New Zealand Knowledge Centre).
Related research
Keywords:
Find related papers by JEL classification: C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Bayesian Analysis C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications
This paper has been announced in the following NEP Reports: