Multi-dimensional monetary policy shocks based on heteroscedasticity

We propose a two-step approach to estimate multi-dimensional monetary policy shocks and their causal effects requiring only daily financial market data and policy events. First, we combine a heteroscedasticity-based identification scheme with recursive zero restrictions along the term structure of interest rates to disentangle multi-dimensional monetary policy shocks and derive an instrumental variables estimator to estimate dynamic causal effects. Second, we propose to use the Kalman filter to compute the linear minimum mean-square-error prediction of the unobserved monetary policy shocks. We apply the approach to examine the causal effects of US monetary policy on the exchange rate. The heteroscedasticity-based monetary policy shocks display a relevant correlation with existing high-frequency surprises. In addition, their dynamic causal effects on the exchange rate are similar. This suggests the approach is a valid alternative if high-frequency identification schemes are not applicable.