L. N. Ferreira, Silvia Miranda-Agrippino, Giovanni Ricco
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引用次数: 7
Abstract
We propose a Bayesian approach to Local Projections that optimally addresses the empirical bias-variance trade-off intrinsic in the choice between direct and iterative methods. Bayesian Local Projections (BLP) regularise LP regressions via informative priors, and estimate impulse response functions that capture the properties of the data more accurately than iterative VARs. BLPs preserve the flexibility of LPs while retaining a degree of estimation uncertainty comparable to Bayesian VARs with standard macroeconomic priors. As regularised direct forecasts, BLPs are also a valuable alternative to BVARs for multivariate out-of-sample projections.
期刊介绍:
The Review of Economics and Statistics is a 100-year-old general journal of applied (especially quantitative) economics. Edited at the Harvard Kennedy School, the Review has published some of the most important articles in empirical economics.