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Autor:innen: 
Giannone, Domenico
Lenza, Michele
Primiceri, Giorgio E.
Datum: 
2012
Schriftenreihe/Nr.: 
ECB Working Paper No. 1494
Verlag: 
European Central Bank (ECB), Frankfurt a. M.
Zusammenfassung: 
Vector autoregressions (VARs) are flexible time series models that can capture complex dynamic interrelationships among macroeconomic variables. However, their dense parameterization leads to unstable inference and inaccurate out-ofsample forecasts, particularly for models with many variables. A solution to this problem is to use informative priors, in order to shrink the richly parameterized unrestricted model towards a parsimonious naïve benchmark, and thus reduce estimation uncertainty. This paper studies the optimal choice of the informativeness of these priors, which we treat as additional parameters, in the spirit of hierarchical modeling. This approach is theoretically grounded, easy to implement, and greatly reduces the number and importance of subjective choices in the setting of the prior. Moreover, it performs very well both in terms of out-of-sample forecasting—as well as factor models—and accuracy in the estimation of impulse response functions.
Schlagwörter: 
Bayesian methods
forecasting
hierarchical modeling
Impulse responses
marginal likelihood
JEL: 
C11
C32
C53
E37
Dokumentart: 
Working Paper

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