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Analysing shock transmission in a data-rich environment: A large BVAR for New Zealand

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Abstract

We analyse a large Bayesian Vector Autoregression (BVAR) containing almost one hundred New Zealand macroeconomic time series. Methods for allowing multiple blocks of equations with block-specific Bayesian priors are described, and forecasting results show that our model compares favourably to a range of other time series models. Examining the impulse responses to a monetary policy shock and to two less conventional shocks – net migration and the climate – we highlight the usefulness of the large BVAR in analysing shock transmission.

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Bibliographic Info

Paper provided by Reserve Bank of New Zealand in its series Reserve Bank of New Zealand Discussion Paper Series with number DP2008/09.

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Length: 34 p.
Date of creation: May 2008
Date of revision:
Handle: RePEc:nzb:nzbdps:2008/09

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Citations

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Cited by:
  1. Sarah Drought & Chris McDonald, 2011. "Forecasting house price inflation: a model combination approach," Reserve Bank of New Zealand Discussion Paper Series DP2011/07, Reserve Bank of New Zealand.
  2. Rangan Gupta & Marius Jurgilas & Alain Kabundi & Stephen M. Miller, 2011. "Monetary policy and housing sector dynamics in a large-scale Bayesian vector autoregressive model," International Journal of Strategic Property Management, Taylor & Francis Journals, vol. 16(1), pages 1-20, August.
  3. Rangan Gupta & Alain Kabundi & Stephen M. Miller, 2009. "Forecasting the US Real House Price Index: Structural and Non-Structural Models with and without Fundamentals," Working papers 2009-42, University of Connecticut, Department of Economics.
  4. Chris Bloor & Troy Matheson, 2009. "Real-time conditional forecasts with Bayesian VARs: An application to New Zealand," Reserve Bank of New Zealand Discussion Paper Series DP2009/02, Reserve Bank of New Zealand.
  5. Adina Popescu & Alina Carare, 2011. "Monetary Policy and Risk-Premium Shocks in Hungary: Results from a Large Bayesian VAR," IMF Working Papers 11/259, International Monetary Fund.
  6. Karlsson, Sune, 2012. "Forecasting with Bayesian Vector Autoregressions," Working Papers 2012:12, Örebro University, School of Business.
  7. Chris Bloor & Chris McDonald, 2013. "Estimating the impacts of restrictions on high LVR lending," Reserve Bank of New Zealand Analytical Notes series AN2013/05, Reserve Bank of New Zealand.
  8. Dimitris Korobilis & Michelle Gilmartin, 2011. "The Dynamic Effects of U.S. Monetary Policy on State Unemployment," Working Paper Series 12_11, The Rimini Centre for Economic Analysis.
  9. John Muellbauer, 2010. "Household decisions, credit markets and the macroeconomy: implications for the design of central bank models," BIS Working Papers 306, Bank for International Settlements.
  10. Balcilar, Mehmet & Gupta, Rangan & Shah, Zahra B., 2011. "An in-sample and out-of-sample empirical investigation of the nonlinearity in house prices of South Africa," Economic Modelling, Elsevier, vol. 28(3), pages 891-899, May.
  11. Rangan Gupta & Marius Jurgilas & Stephen M. Miller & Dylan van Wyk, 2010. "Financial Market Liberalization, Monetary Policy, and Housing Price Dynamics," Working Papers 201009, University of Pretoria, Department of Economics.
  12. Rangan Gupta, 2012. "Forecasting House Prices for the Four Census Regions and the Aggregate US Economy: The Role of a Data-Rich Environment," Working Papers 201214, University of Pretoria, Department of Economics.
  13. Güneş Kamber & Chris McDonald & Gael Price, 2013. "Drying out: Investigating the economic effects of drought in New Zealand," Reserve Bank of New Zealand Analytical Notes series AN2013/02, Reserve Bank of New Zealand.

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