Sign Restrictions, Structural Vector Autoregressions, and Useful Prior Information ∗

By Christiane Baumeister, James D. Hamilton. 2015

361 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Marketing

View Paper: https://www.semanticscholar.org/paper/4860e3b9a20ba1144e9758a860be06475db70fe7

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Citations in the corpus

The systematic component of monetary policy in SVARs: An agnostic identification procedure (2015, neutral): “Baumeister and Hamilton (2014) study how informative the data are relative to the prior distributions on the structural parameters in the estimation of SVARs identified using sign restrictions.”
Bayesian inference on structural impulse response functions (2019, methodology): “Baumeister & Hamilton (2015b), who improve on the method of Sims & Zha (1998), persuasively argue for an explicit Bayesian approach to imposing prior information.”
If the Fed Sneezes, Who Catches a Cold? (2017, background): “Recently, Baumeister and Hamilton (2015) have argued that sign restrictions can unduly constrain impulse response posteriors.”
Uncertainty and Business Cycles: Exogenous Impulse or Endogenous Response? (2015, neutral): “Baumeister and Hamilton (2015) note that an uninformative prior over Q can be informative for the posterior over the structural impact matrix and impulse responses in sign-restricted SVARs.”
Inference in structural vector autoregressions when the identifying assumptions are not fully believed: Re-evaluating the role of monetary policy in economic fluctuations (2018, methodology): “Baumeister and Hamilton (2015) showed that the mean m∗i and variance diiM ∗ i of the posterior distribution p(bi|A,D,YT ) for the lagged coefficients of the ith structural equation can be found from an OLS regression of Ỹi in their equation (47) on X̃i in equation (48).”
Macroeconomic Shocks and Their Propagation (2016, methodology): “For example, Arias et al. (2015b) propose methods involving agnostic priors in one dimension and Baumeister and Hamilton (2015) propose methods involving agnostic priors in another dimension.”
Theory and Practice of GVAR Modeling (2016, neutral): “See Baumeister and Hamilton (2014). Identification of shocks in a GVAR is subject to the same issues as in standard VARs, but is further complicated due to the cross-country interactions and the high dimensionality of the model.”
Oil price elasticities and oil price fluctuations (2016, neutral): “In recent work, Baumeister and Hamilton (2015b) acknowledge the importance of specifying plausible priors on both the oil supply and demand elasticities.16 What emerges from our analysis is that one could use the joint distribution of the oil supply and demand elasticities to narrow down the set of…”
Delta-method inference for a class of set-identified SVARs (2018, methodology): “Most studies working with this type of models have relied on Bayesian methods to construct posterior credible sets for the structural parameters of interest (for example, Inoue and Kilian (2013), Arias, Rubio-Ramirez, and Waggoner (2017), and Baumeister and Hamilton (2015)).”