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Real-time conditional forecasts with Bayesian VARs: An application to New Zealand

  • Bloor, Chris
  • Matheson, Troy

Abstract We develop a Bayesian VAR (BVAR) to produce conditional forecasts for the New Zealand economy. In a real-time out-of-sample forecasting exercise, we find that the BVAR outperforms a selection of other time series models, and it yields forecasts of similar accuracy to the forecasts produced internally at the Reserve Bank of New Zealand. Examining shock decompositions, we also highlight the importance of foreign shocks for the New Zealand economy. Our results suggest that the BVAR is a useful tool for policy making in real time.

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Article provided by Elsevier in its journal The North American Journal of Economics and Finance.

Volume (Year): 22 (2011)
Issue (Month): 1 (January)
Pages: 26-42

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Handle: RePEc:eee:ecofin:v:22:y:2011:i:1:p:26-42
Contact details of provider: Web page: http://www.elsevier.com/locate/inca/620163

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  1. Galí, Jordi & Monacelli, Tommaso, 2002. "Monetary Policy and Exchange Rate Volatility in a Small Open Economy," CEPR Discussion Papers 3346, C.E.P.R. Discussion Papers.
  2. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
  3. Troy Matheson, 2005. "Factor model forecasts for New Zealand," Reserve Bank of New Zealand Discussion Paper Series DP2005/01, Reserve Bank of New Zealand.
  4. Christopher A. Sims, 1989. "A nine variable probabilistic macroeconomic forecasting model," Discussion Paper / Institute for Empirical Macroeconomics 14, Federal Reserve Bank of Minneapolis.
  5. David O. Cushman & Tao Zha, 1995. "Identifying monetary policy in a small open economy under flexible exchange rates," FRB Atlanta Working Paper 95-7, Federal Reserve Bank of Atlanta.
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  8. Chris Bloor & Troy Matheson, 2010. "Analysing shock transmission in a data-rich environment: a large BVAR for New Zealand," Empirical Economics, Springer, vol. 39(2), pages 537-558, October.
  9. Clarida, Richard & Galí, Jordi & Gertler, Mark, 1999. "The Science of Monetary Policy: A New Keynesian Perspective," CEPR Discussion Papers 2139, C.E.P.R. Discussion Papers.
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  11. Christiano, Lawrence J. & Eichenbaum, Martin & Evans, Charles L., 1999. "Monetary policy shocks: What have we learned and to what end?," Handbook of Macroeconomics, in: J. B. Taylor & M. Woodford (ed.), Handbook of Macroeconomics, edition 1, volume 1, chapter 2, pages 65-148 Elsevier.
  12. Waggoner, Daniel F. & Zha, Tao, 2003. "A Gibbs sampler for structural vector autoregressions," Journal of Economic Dynamics and Control, Elsevier, vol. 28(2), pages 349-366, November.
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  15. Kadiyala, K Rao & Karlsson, Sune, 1997. "Numerical Methods for Estimation and Inference in Bayesian VAR-Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(2), pages 99-132, March-Apr.
  16. John C. Robertson & Ellis W. Tallman, 1999. "Vector autoregressions: forecasting and reality," Economic Review, Federal Reserve Bank of Atlanta, issue Q1, pages 4-18.
  17. Matheson, Troy D., 2010. "An analysis of the informational content of New Zealand data releases: The importance of business opinion surveys," Economic Modelling, Elsevier, vol. 27(1), pages 304-314, January.
  18. Zha, Tao, 1999. "Block recursion and structural vector autoregressions," Journal of Econometrics, Elsevier, vol. 90(2), pages 291-316, June.
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