RWP 18-13, December 2018; Updated February 2022
Expectations play a central role in macroeconomics. Expectations are empirically measured from surveys or financial markets and are frequently analyzed in Vector autoregression (VAR) models alongside realized data of the same variable. However, this leads to two different expectations for the same variable: the VAR-based forecast and the external forecast. This paper proposes a Bayesian prior over the VAR parameters which allows for varying degrees of consistency between these two forecasts. As we demonstrate in two applications, our approach can sharpen structural VAR identification of forward guidance shocks and enhances VAR forecasts of inflation tail risks.
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JEL Classification: C11, C32, E52, E31
Doh, Taeyoung, and A. Lee Smith. “A New Approach to Integrating Expectations into VAR Models.” Federal Reserve Bank of Kansas City, Research Working Paper no. 18-13, December; updated February 2022. Available at External Linkhttps://doi.org/10.18651/RWP2018-13