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

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  • Chris Bloor
  • Troy Matheson

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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

Article provided by Springer in its journal Empirical Economics.

Volume (Year): 39 (2010)
Issue (Month): 2 (October)
Pages: 537-558

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Handle: RePEc:spr:empeco:v:39:y:2010:i:2:p:537-558

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Keywords: Bayesian VAR; Impulse responses; C11; C13; C33; C53;

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References

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Citations

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Cited by:
  1. Bloor, Chris & Matheson, Troy, 2011. "Real-time conditional forecasts with Bayesian VARs: An application to New Zealand," The North American Journal of Economics and Finance, Elsevier, vol. 22(1), pages 26-42, January.
  2. 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.
  3. 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.
  4. Mehmet Balcilar & Rangan Gupta & Zahra Shah, 2010. "An In-Sample and Out-of-Sample Empirical Investigation of the Nonlinearity in House Prices of South Africa," Working Papers 201008, University of Pretoria, Department of Economics.
  5. 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.
  6. 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.
  7. 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.
  8. Nicholas Sander, 2013. "Migration and the housing market," Reserve Bank of New Zealand Analytical Notes series AN2013/10, Reserve Bank of New Zealand.
  9. 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.
  10. 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.
  11. Dimitris Korobilis & Michelle Gilmartin, 2011. "The Dynamic Effects of U.S. Monetary Policy on State Unemployment," Working Paper Series, The Rimini Centre for Economic Analysis 12_11, The Rimini Centre for Economic Analysis.
  12. 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.
  13. Karlsson, Sune, 2012. "Forecasting with Bayesian Vector Autoregressions," Working Papers, Örebro University, School of Business 2012:12, Örebro University, School of Business.
  14. Adina Popescu & Alina Carare, 2011. "Monetary Policy and Risk-Premium Shocks in Hungary," IMF Working Papers 11/259, International Monetary Fund.

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