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)

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)

Structural Vector Autoregressive Models

2020 ◽  
Author(s):  
Luca Gambetti
Keyword(s):  
Linear Functions ◽  
Impulse Response Functions ◽  
Vector Autoregressions ◽  
Structural Shocks ◽  
Vector Autoregressive ◽  
Varying Coefficients ◽  
Macroeconomic Analysis ◽  
Long Run ◽  
Time Varying Coefficients ◽  
Structural Vector

Structural vector autoregressions (SVARs) represent a prominent class of time series models used for macroeconomic analysis. The model consists of a set of multivariate linear autoregressive equations characterizing the joint dynamics of economic variables. The residuals of these equations are combinations of the underlying structural economic shocks, assumed to be orthogonal to each other. Using a minimal set of restrictions, these relations can be estimated—the so-called shock identification—and the variables can be expressed as linear functions of current and past structural shocks. The coefficients of these equations, called impulse response functions, represent the dynamic response of model variables to shocks. Several ways of identifying structural shocks have been proposed in the literature: short-run restrictions, long-run restrictions, and sign restrictions, to mention a few. SVAR models have been extensively employed to study the transmission mechanisms of macroeconomic shocks and test economic theories. Special attention has been paid to monetary and fiscal policy shocks as well as other nonpolicy shocks like technology and financial shocks. In recent years, many advances have been made both in terms of theory and empirical strategies. Several works have contributed to extend the standard model in order to incorporate new features like large information sets, nonlinearities, and time-varying coefficients. New strategies to identify structural shocks have been designed, and new methods to do inference have been introduced.

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
Sign in / Sign up

Export Citation Format

Share Document