Sign Restrictions, Structural Vector Autoregressions, and Useful Prior Information

Econometrica ◽  
2015 ◽  
Vol 83 (5) ◽  
pp. 1963-1999 ◽  
Author(s):  
Christiane Baumeister ◽  
James D. Hamilton
Keyword(s):  
Prior Information ◽  
Vector Autoregressions ◽  
Sign Restrictions ◽  
Structural Vector

scholarly journals Sign Restrictions in Structural Vector Autoregressions: A Critical Review

2011 ◽  
Vol 49 (4) ◽  
pp. 938-960 ◽  
Author(s):  
Renée Fry ◽  
Adrian Pagan
Keyword(s):  
Model Identification ◽  
Parametric Identification ◽  
Identification Problem ◽  
Structural Systems ◽  
Impulse Responses ◽  
Vector Autoregressions ◽  
Sign Restrictions ◽  
Unique Model ◽  
Jel C32 ◽  
Structural Vector

The paper provides a review of the estimation of structural vector autoregressions with sign restrictions. It is shown how sign restrictions solve the parametric identification problem present in structural systems but leaves the model identification problem unresolved. A market and a macro model are used to illustrate these points. Suggestions have been made on how to find a unique model. These are reviewed. An analysis is provided of whether one can recover the true impulse responses and what difficulties might arise when one wishes to use the impulse responses found with sign restrictions. (JEL C32, C51, E12)

scholarly journals Narrative Sign Restrictions for SVARs

2018 ◽  
Vol 108 (10) ◽  
pp. 2802-2829 ◽  
Author(s):  
Juan Antolín-Díaz ◽  
Juan F. Rubio-Ramírez
Keyword(s):  
Vector Autoregressions ◽  
Structural Shocks ◽  
Narrative Methods ◽  
Oil Market ◽  
Sign Restrictions ◽  
Jel C32 ◽  
Historical Decomposition ◽  
Sign Restriction ◽  
Structural Vector ◽  
Narrative Account

We identify structural vector autoregressions using narrative sign restrictions. Narrative sign restrictions constrain the structural shocks and/or the historical decomposition around key historical events, ensuring that they agree with the established narrative account of these episodes. Using models of the oil market and monetary policy, we show that narrative sign restrictions tend to be highly informative. Even a single narrative sign restriction may dramatically sharpen and even change the inference of SVARs originally identified via traditional sign restrictions. Our approach combines the appeal of narrative methods with the popularized usage of traditional sign restrictions. (JEL C32, E52, Q35, Q43)

A Comparison of Approaches to Select the Informativeness of Priors in BVARs

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Jan Prüser ◽  
Christoph Hanck
Keyword(s):  
Impulse Response ◽  
Prior Information ◽  
Hierarchical Modeling ◽  
Small Samples ◽  
Model Parameters ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Vector Autoregressions ◽  
The Rich ◽  
Bayesian Vars

Abstract Vector autoregressions (VARs) are richly parameterized time series models that can capture complex dynamic interrelationships among macroeconomic variables. However, in small samples the rich parametrization of VAR models may come at the cost of overfitting the data, possibly leading to imprecise inference for key quantities of interest such as impulse response functions (IRFs). Bayesian VARs (BVARs) can use prior information to shrink the model parameters, potentially avoiding such overfitting. We provide a simulation study to compare, in terms of the frequentist properties of the estimates of the IRFs, useful strategies to select the informativeness of the prior. The study reveals that prior information may help to obtain more precise estimates of impulse response functions than classical OLS-estimated VARs and more accurate coverage rates of error bands in small samples. Strategies based on selecting the prior hyperparameters of the BVAR building on empirical or hierarchical modeling perform particularly well.

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