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.