Inference for VARs identified with sign restrictions

Eleonora Granziera, Hyungsik Roger Moon, Frank Schorfheide

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8 Citations (Scopus)

Abstract

There is a fast growing literature that set-identifies structural vector autoregressions (SVARs) by imposing sign restrictions on the responses of a subset of the endogenous variables to a particular structural shock (sign-restricted SVARs). Most methods that have been used to construct pointwise coverage bands for impulse responses of sign-restricted SVARs are justified only from a Bayesian perspective. This paper demonstrates how to formulate the inference problem for sign-restricted SVARs within a moment-inequality framework. In particular, it develops methods of constructing confidence bands for impulse response functions of sign-restricted SVARs that are valid from a frequentist perspective. The paper also provides a comparison of frequentist and Bayesian coverage bands in the context of an empirical application—the former can be substantially wider than the latter.

Original languageEnglish
Pages (from-to)1087-1121
Number of pages35
JournalQuantitative Economics
Volume9
Issue number3
DOIs
Publication statusPublished - 2018 Nov

Bibliographical note

Funding Information:
Eleonora Granziera: eleonora.granziera@bof.fi Hyungsik Roger Moon: moonr@usc.edu Frank Schorfheide: schorf@ssc.upenn.edu We thank Andres Santos (coeditor), Fabio Canova, Eric Renault, Paul Sangrey, James Stock, several anonymous referees, as well as participants at various conferences and seminars for helpful comments. We also thank Mihye Lee for her contributions to the first draft of this paper and Minchul Shin for research assistance. Schorfheide gratefully acknowledges financial support from the National Science Foundation under Grants SES 1061725 and 1424843. The views expressed in this paper are those of the authors and do not necessarily reflect those of the Bank of Finland. The Online Technical Appendix as well as data and software to replicate the empirical analysis are available in the Supplementary Material (Granziera, Moon, and Schorfheide (2018)).

Publisher Copyright:
Copyright © 2018 The Authors.

All Science Journal Classification (ASJC) codes

  • Economics and Econometrics

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