A Theory Of Data-Oriented Identification With A SVAR Application

59 Pages Posted: 28 Nov 2014 Last revised: 15 Mar 2015

See all articles by Nikolay Arefiev

Nikolay Arefiev

National Research University Higher School of Economics

Date Written: November 28, 2014

Abstract

I propose a method identification of structural vector autoregressions (SVARs) and simultaneous equations models (SEMs) with orthogonal structural shocks using testable identification restrictions. If some sparsity conditions are satisfied, the method produces a set of testable inclusions and exclusions, sufficient for the full identification. The method stems from the theory of probabilistic graphical models and from the theory of identification of SVARs and SEMs, merging them into a unified approach. In the application example, I estimate a SVAR monetary model of the US economy with 6 variables, where all but one identifying restrictions are testable. The method produces relatively narrow confidence intervals for the impulse-response functions, does not generate any anomalies such as the price puzzle, and reveals importance of informational channels through which news about structural shocks spread throughout the economy.

Keywords: Identification, instrumental variables, data-oriented identification, sparse structural models, structural vector autoregression, SVAR, simultaneous equations model, SEM, probabilistic graphical model, PGM

JEL Classification: C30, E31, E52

Suggested Citation

Arefiev, Nikolay, A Theory Of Data-Oriented Identification With A SVAR Application (November 28, 2014). Higher School of Economics Research Paper No. WP BRP 79/EC/2014 . Available at SSRN: https://ssrn.com/abstract=2531613 or http://dx.doi.org/10.2139/ssrn.2531613

Nikolay Arefiev (Contact Author)

National Research University Higher School of Economics ( email )

Myasnitskaya street, 20
Moscow, Moscow 119017
Russia

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