Narrative Sign Restrictions for SVARs

Antolín-Díaz, Juan; Rubio-Ramı́rez, Juan Francisco

doi:10.1257/aer.20161852

Cited by 263 publications

(167 citation statements)

References 34 publications

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“…There are few ecological, environmental, or resource economics publications in flagship journals within economics. For example, in 2018 only two papers published in the American Economic Review listed classification codes for renewable resources and conservation, nonrenewable resources and conservation, energy economics, or environmental economics (57,58). Only a small minority of the top economics departments have fields in ecological, environmental, or resource economics.…”

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confidence: 99%

Role of economics in analyzing the environment and sustainable development

Polasky

Kling

Levin

et al. 2019

Proc. Natl. Acad. Sci. U.S.A.

177

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mentioning

confidence: 99%

Role of economics in analyzing the environment and sustainable development

Polasky

Kling

Levin

et al. 2019

Proc. Natl. Acad. Sci. U.S.A.

177

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“…Traditional narrative analyses, such as Romer and Romer (), are limited to analyzing impulse‐response‐functions to a single shock. Antolin‐Diaz and Rubio‐Ramirez () also analyzed impulse‐response functions in VARs, but they only look at a few, large realizations of a shock. Compared to my approach, the merit of these limited information approaches is that they get by with partially specified models.…”

Section: Narrative Dsge‐svars: the Ideamentioning

confidence: 99%

A narrative approach to a fiscal DSGE model

Drautzburg

2020

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Structural DSGE models are used for analyzing both policy and the sources of business cycles. Conclusions based on full structural models are, however, potentially affected by misspecification. A competing method is to use partially identified SVARs based on narrative shocks. This paper asks whether both approaches agree. Specifically, I use narrative data in a DSGE-SVAR that partially identify policy shocks in the VAR and assess the fit of the DSGE model relative to this narrative benchmark. In developing this narrative DSGE-SVAR, I develop a tractable Bayesian approach to proxy VARs and show that such an approach is valid for models with a certain class of Taylor rules. Estimating a DSGE-SVAR based on a standard DSGE model with fiscal rules and narrative data, I find that the DSGE model identification is at odds with the narrative information as measured by the marginal likelihood. I trace this discrepancy to differences in impulse responses, identified historical shocks and policy rules. The results indicate monetary accommodation of fiscal shocks.Narrative approach to a fiscal DSGE model 803 signal-to-noise ratio of the measurement equation. With a proper prior, the models both here and in Caldara and Herbst (2019) require a Metropolis-within-Gibbs sampler. Unlike their setting, I allow for possibly missing instruments, an important issue in applied work.My result that the proxy SVAR identification is valid for a class of DSGE models is the second building block for my DSGE-SVAR: I provide conditions under which the instrument-identified VAR in Mertens and Ravn (2013) correctly identifies shocks and policy rule coefficients in models with standard Taylor-type policy rules, such as Leeper, Plante, and Traum (2010) and Fernandez-Villaverde, Guerron-Quintana, Kuester, and Rubio-Ramirez (2015). This property of the narrative VAR contrasts with traditional VARs that identify shocks through contemporaneous zero restrictions. DSGE models that match the VAR then need to assume that economic agents only react to policy shocks with a delay. The narrative VAR approach is valid if the data are generated from a widely used class of DSGE models, without restricting the timing. The key condition for my result is that the information set in the VAR captures the variables policy-makers pay attention to. This theoretical result mirrors the empirical result in Caldara and Herbst (2019) that credit spreads may be an important policy variable.My application contributes to the literature on fiscal and monetary policy DSGE models, which is important from a substantive point of view: With monetary policy constrained by the zero lower bound (ZLB), "stimulating" fiscal policy has gained a lot of attention and influential papers such as Christiano, Eichenbaum, and Rebelo (2011) have used quantitative DSGE models for the analysis of fiscal policies. Since the fiscal building blocks of DSGE models are less well studied than, say, the Taylor rule for monetary policy (e.g., Clarida, Galí, and Gertler (2000)), assessing the fiscal policy im...

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“…The problem 2.7 can be solved using numerical optimisation techniques. The method draws on the technique of matching impulse response functions (Christiano, Eichenbaum, and Evans, 2005), as well as on recent orthogonalisation schemes that use event-related restrictions (Ludvigson, Ma, andNg, 2015, 2017;Antolin-Diaz and Rubio-Ramirez, 2018;Ben Zeev, 2018). The proposed orthogonalisation scheme is general, and could be used to explore whether the driving force of one particular historical event can account for the causes of other, seemingly similar, historical events as well.…”

Section: The Recession-shockmentioning

confidence: 99%

“…The method to construct the γ-shock draws on the macroeconomic literature on the identification of news shocks (Uhlig 2004; Barsky and Sims 2011;Kurmann and Otrok 2013); I combine the ideas from this literature with predictive regressions of the asset pricing literature (Campbell and Shiller 1988;Goyal and Welch 2008;Pastor and Stambaugh 2009). The method to identify a Recession-shock based on matching historical decompositions is inspired by papers matching impulse response functions (Christiano, Eichenbaum, and Evans, 2005) and the recent structural VAR literature (Ludvigson, Ma, and Ng, 2017;Antolin-Diaz and Rubio-Ramirez, 2018;Ben Zeev, 2018).…”

Section: Introductionmentioning

confidence: 99%

Macroeconomic Shocks and Risk Premia

Pintér

2018

SSRN Journal

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What are the macroeconomic forces behind the cross-sectional and time-series variation in expected excess returns? To answer this question, this paper integrates models of empirical asset pricing with structural vector autoregressions (VAR). First, I use an unconditional asset pricing framework to construct an orthogonal shock in a macroeconomic VAR that best explains the cross-sectional variation in expected returns. The obtained "λ-shock" closely resembles identified monetary policy surprises and does not explain the recent US recessions. Second, I integrate return-forecasting methods to construct a second shock in the VAR, which best explains time-variation in expected returns. The obtained "γ-shock" turns out to be virtually orthogonal to the λ-shock, closely resembles demand-type financial shocks identified by macroeconomists, and explains most US recessions. I find that the λ-shock and the γ-shock jointly explain up to 80% of aggregate consumption fluctuations in the US.

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Narrative Sign Restrictions for SVARs

Cited by 263 publications

References 34 publications

Role of economics in analyzing the environment and sustainable development

Role of economics in analyzing the environment and sustainable development

A narrative approach to a fiscal DSGE model

Macroeconomic Shocks and Risk Premia

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