Working Paper: NBER ID: w18467
Authors: Domenico Giannone; Michele Lenza; Giorgio E. Primiceri
Abstract: 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-of-sample 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 naìˆve 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.
Keywords: Bayesian VAR; Hierarchical modeling; Forecasting; Impulse response functions
JEL Codes: C11; C32; C53; E37; E47
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Informative priors (D83) | Estimation uncertainty (C13) |
| Informative priors (D83) | Out-of-sample forecasting accuracy (C53) |
| Hierarchical BVAR approach (C32) | Point and density forecasts (C53) |
| Hierarchical BVAR approach (C32) | Impulse response estimates (C51) |
| Tighter priors (C11) | Estimation of coefficients (C51) |
| Hierarchical BVAR (C32) | Performance against flat-prior VARs (C32) |
| Hierarchical modeling framework (C59) | Forecasting performance of BVARs (C53) |
| Hierarchical BVAR (C32) | Impulse responses to structural shocks (C22) |