Bayesian Flexible Local Projections

Abstract We develop a methodology to estimate impulse response functions via Bayesian techniques with the goal of providing a bridge between a linear vector autoregressive specification and a high-order polynomial local projection, namely flexible local projection. We label this methodology Bayesian Flexible Local Projection (BFLP). We assess the properties of BFLP in a Monte Carlo framework considering both linear and non-linear models as data generating processes. We also empirically illustrate how BFLP can be used with standard identification strategies. In particular, we show how to use external instruments to identify the effects of the monetary policy shock in the United States. Furthermore, exploiting the time-varying nature of the impulse response functions based on BFLP, we assess the zero lower bound irrelevance hypothesis and find no strong evidence that monetary policy was less effective in influencing output and inflation during the recent ZLB period.