Skip to main content

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


We analyse a large Bayesian Vector Autoregression (BVAR) containing almost 100 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.

This is a preview of subscription content, access via your institution.


  1. Banbura M, Gianonne D, Reichlin L (2009) Large Bayesian VARs. J Appl Econom (forthcoming)

  2. Banerjee A, Marcellino M (2008) Factor-augmented error correction models. Discussion paper 6707, Centre for Economic Policy Research (CEPR)

  3. Bernanke B, Boivin J, Eliasz PS (2005) Measuring the effects of monetary policy: a factor-augmented vector autoregressive (FAVAR) approach. Q J Econ 120(1): 387–422

    Article  Google Scholar 

  4. Boivin J, Giannoni M (2008) Global forces and monetary policy effectiveness. Working paper 13736, National Bureau of Economic Research

  5. Buckle RA, Kim K, Kirkham H, McLellan N, Sharma J (2007) A structural VAR business cycle model of a volatile small open economy. Econ Model 24: 990–1017

    Article  Google Scholar 

  6. Christiano LJ, Eichenbaum M, Evans C (1999) Monetary policy shocks: what have we learned and to what end?. In: Taylor GS, Woodford M (eds) Handbook of macroeconomics, vol 1, chap 2. Elsevier, North-Holland, pp 65–148

    Google Scholar 

  7. Christiano LJ, Eichenbaum M, Evans C (2005) Nominal rigidities and the dynamic effects of a shock to monetary policy. J Polit Econ 113(1): 1–45

    Article  Google Scholar 

  8. Coleman A, Landon-Lane J (2007) Housing markets and migration in New Zealand, 1962–2006. Discussion paper 2007/12, Reserve Bank of New Zealand

  9. Cushman DO, Zha T (1997) Identifying monetary policy in a small open economy under flexible exchange rates. J Monet Econ 39: 433–448

    Article  Google Scholar 

  10. De Mol C, Gianonne D, Reichlin L (2008) Forecasting using a large number of predictors: is Bayesian regression a valid alternative to Principal Components? J Econom (forthcoming)

  11. Del Negro M, Schorfheide F (2004) Priors from general equilibrium models for VARs. Int Econ Rev 45(2): 643–673

    Article  Google Scholar 

  12. Diebold FX, Mariano RS (1995) Comparing predictive accuracy. J Bus Econ Stat 13(3): 253–263

    Article  Google Scholar 

  13. Doan T, Litterman R, Sims C (1984) Forecasting and conditional projections using realistic prior distributions. Econom Rev 3: 1–100

    Article  Google Scholar 

  14. Forni M, Hallin M, Lippi M, Reichlin L (2000) The generalized factor model: identification and estimation. Rev Econ Stat 82: 540–554

    Article  Google Scholar 

  15. Forni M, Hallin M, Lippi M, Reichlin L (2005) The generalized factor model: one-sided estimation and forecasting. J Am Stat Assoc 100(471): 830–840

    Article  Google Scholar 

  16. Haug AA, Smith C (2007) Local linear impulse responses for a small open economy. Discussion paper 2007/09, Reserve Bank of New Zealand

  17. Kadiyala KR, Karlsson S (1997) Numerical methods for estimation and inference in Bayesian VAR-models. J Appl Econom 12(2): 99–132

    Article  Google Scholar 

  18. Krolzig H-M (2001) General-to-specific reductions of vector autoregressive processes. Comput Econ Financ 164, Society for Computational Economics

  19. Litterman R (1986) Forecasting with bayesian vector autoregressions—five years of experience. J Bus Econ Stat 4: 25–38

    Article  Google Scholar 

  20. Newey WK, West K (1987) A simple, positive semidefinite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica 55: 703–708

    Article  Google Scholar 

  21. Robertson JC, Tallman EW (1999) Vector autoregressions: forecasting and reality. Fed Reserv Bank Atlanta Econ Rev First Quarter:4–18

  22. Sims CA (1993) A nine-variable probabilistic macroeconomic forecasting model. In: Stock JH, Watson MW (eds) Business cycles, indicators, and forecasting. University of Chicago Press, Chicago

    Google Scholar 

  23. Sims CA, Zha T (1998) Bayesian methods for dynamic multivariate analysis. Int Econ Rev 39(4): 949–968

    Article  Google Scholar 

  24. Stock JH, Watson MW (1999) Forecasting inflation. J Monet Econ 44(2): 293–335

    Article  Google Scholar 

  25. Stock JH, Watson MW (2002) Macroeconomic forecasting using diffusion indexes. J Bus Econ Stat 20(2): 147–162

    Article  Google Scholar 

  26. Stock JH, Watson MW (2005) Implications of dynamic factor models for VAR analysis. Working paper 11467, National Bureau of Economic Research

  27. Waggoner DF, Zha T (2003) A Gibbs sampler for structural vector autoregressions. J Econ Dyn Control 28: 349–366

    Article  Google Scholar 

  28. Zha T (1999) Block recursion and structural vector autoregressions. J Econom 90: 291–316

    Article  Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Troy Matheson.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Bloor, C., Matheson, T. Analysing shock transmission in a data-rich environment: a large BVAR for New Zealand. Empir Econ 39, 537–558 (2010).

Download citation


  • Bayesian VAR
  • Impulse responses

JEL Classification

  • C11
  • C13
  • C33
  • C53