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)

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 Identification of time-varying systems with partial acceleration measurements by synthesis of wavelet decomposition and Kalman filter

2020 ◽  
Vol 12 (6) ◽  
pp. 168781402093046
Author(s):  
Siyi Chen ◽  
Jubin Lu ◽  
Ying Lei
Keyword(s):  
Kalman Filter ◽  
Degrees Of Freedom ◽  
Structural Parameters ◽  
Parametric Identification ◽  
Identification Problem ◽  
Structural Systems ◽  
Wavelet Expansion ◽  
Time Varying ◽  
Multiresolution Decomposition ◽  
Original Time

Structural systems often exhibit time-varying dynamic characteristics during their service life due to serve hazards and environmental erosion, so the identification of time-varying structural systems is an important research topic. Among the previous methodologies, wavelet multiresolution analysis for time-varying structural systems has gained increasing attention in the past decades. However, most of the existing wavelet-based identification approaches request the full measurements of structural responses including acceleration, velocity, and displacement responses at all dynamic degrees of freedom. In this article, an improved algorithm is proposed for the identification of time-varying structural parameters using only partial measurements of structural acceleration responses. The proposed algorithm is based on the synthesis of wavelet multiresolution decomposition and the Kalman filter approach. The time-varying structural stiffness and damping parameters are expanded at multi-scale profile by wavelet multiresolution decomposition, so the time-varying parametric identification problem is converted into a time-invariant one. Structural full responses are estimated by Kalman filter using partial observations of structural acceleration responses. The scale coefficients by the wavelet expansion are estimated via the solution of a nonlinear optimization problem of minimizing the errors between estimated and observed accelerations. Finally, the original time-varying parameters can be reconstructed. To demonstrate the efficiency of the proposed algorithm, the identification of several numerical examples with various time-varying scenarios is studied.

Nonlinear Identification Approach of Nonlinear Constitutive Model of Soil Material with Optimization Procedure

2012 ◽  
Vol 461 ◽  
pp. 686-689
Author(s):  
Li Juan Cao ◽  
Zi Chang Shangguan ◽  
Shou Ju Li
Keyword(s):  
Constitutive Model ◽  
Model Identification ◽  
Optimization Procedure ◽  
Parametric Identification ◽  
Identification Problem ◽  
Identification Algorithm ◽  
Nonlinear Constitutive Model ◽  
Parameter Identification Problem ◽  
Identification Approach ◽  
Identification Techniques

Model identification of dynamic systems in the vibration engineering field has been followed with interest in recent years. A number of identification techniques on this topic are now available, such as parametric or non-parametric identification methods, time domain or frequency domain estimation approaches, etc. The identification approach of nonlinear constitutive model from input-output measurements is proposed. The inverse problem of material characterization is formulated as parameter identification problem that is solved by using optimization procedure. A set of parameters corresponding to the material property can be determined by minimizing objective function which accounts for experimental data and calculated responses of the mechanical model. The performances of the proposed identification approach were evaluated with simulating data. The effectiveness of identification approach is validated by numerical simulation. The investigation results show that the proposed identification algorithm poses good robustness and high identification precision.

scholarly journals ROBUST INFERENCE IN STRUCTURAL VECTOR AUTOREGRESSIONS WITH LONG-RUN RESTRICTIONS

2019 ◽  
Vol 36 (1) ◽  
pp. 86-121
Author(s):  
Guillaume Chevillon ◽  
Sophocles Mavroeidis ◽  
Zhaoguo Zhan
Keyword(s):  
Unit Roots ◽  
Confidence Bands ◽  
Nuisance Parameters ◽  
Robust Methods ◽  
Impulse Responses ◽  
Vector Autoregressions ◽  
Weak Identification ◽  
Popular Method ◽  
Long Run ◽  
Structural Vector

Long-run restrictions are a very popular method for identifying structural vector autoregressions, but they suffer from weak identification when the data is very persistent, i.e., when the highest autoregressive roots are near unity. Near unit roots introduce additional nuisance parameters and make standard weak-instrument-robust methods of inference inapplicable. We develop a method of inference that is robust to both weak identification and strong persistence. The method is based on a combination of the Anderson-Rubin test with instruments derived by filtering potentially nonstationary variables to make them near stationary using the IVX instrumentation method of Magdalinos and Phillips (2009). We apply our method to obtain robust confidence bands on impulse responses in two leading applications in the literature.

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