Are spectral estimators useful for long-run restrictions in SVARs?

Mertens, Elmar

doi:10.1016/j.jedc.2012.06.007

Cited by 6 publications

(4 citation statements)

References 38 publications

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“…2020 Christiano, Eichenbaum, and Vigfusson (2006) and Mertens (2012) made the related point that SVAR‐based long‐run identification could employ nonparametric estimators of the long‐run variance matrix instead of the VAR estimator. …”

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confidence: 99%

Local Projections and VARs Estimate the Same Impulse Responses

Plagborg‐Møller¹,

Wolf

2021

ECTA

383

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We prove that local projections (LPs) and Vector Autoregressions (VARs) estimate the same impulse responses. This nonparametric result only requires unrestricted lag structures. We discuss several implications: (i) LP and VAR estimators are not conceptually separate procedures; instead, they are simply two dimension reduction techniques with common estimand but different finite‐sample properties. (ii) VAR‐based structural identification—including short‐run, long‐run, or sign restrictions—can equivalently be performed using LPs, and vice versa. (iii) Structural estimation with an instrument (proxy) can be carried out by ordering the instrument first in a recursive VAR, even under noninvertibility. (iv) Linear VARs are as robust to nonlinearities as linear LPs.

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confidence: 99%

Local Projections and VARs Estimate the Same Impulse Responses

Plagborg‐Møller¹,

Wolf

2021

ECTA

383

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“…However, Mertens [5] shows that the CEV procedure (fully described by 9) fails to properly utilize this additional information in the estimation of the impact vector. Consequently, the improved small-sample results of the CEV method do not extend to a wider range of parameterizations of the RBC model.…”

Section: Standard Methodsmentioning

confidence: 99%

“…A number of recent studies have called into question the plausibility of identifying technology shocks by imposing long-run restrictions on VARs. Demirel [4], Mertens [5], and Chari et al [6] find that the standard long-run identification approach can be highly inaccurate if the number of lags included into the estimated VAR is smaller than the number of lags involved with the actual data-generating process. Since in many RBC models the reduced-form dynamics of the observed variables can only be captured by an infinite-order VAR, this type of mismatch between the estimated and actual lag structures is often relevant (see [6][7][8][9]).…”

Section: Introductionmentioning

confidence: 99%

On the Identification of Technology Shocks: An Alternative to the Standard Long-Run Method

Demirel¹

2012

TEL

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This study proposes an alternative procedure to identify technology shocks using vector autoregressions (VARs). The proposed procedure delivers improved small-sample properties relative to the standard long-run identification method provided that the dynamics of the observed variables can only be captured precisely by an infinite-order VAR. Monte Carlo experiments on artificial data produced by a standard version of the real business cycle model demonstrate that the proposed procedure is associated with smaller average bias and mean square error. These results obtain under a range of specifications regarding the share of technology shocks in overall output variability.

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“…Plugging this expression for the structural shocks into the state equation (2.1) and rearranging, criteria helps in reducing the truncation bias. Using nonparametric approaches Christiano et al (2007) and Mertens (2012) nd mixed results.…”

Section: Background: Invertibility Nonfundamentalness and Lag Truncamentioning

confidence: 99%

Measuring nonfundamentalness for structural VARs

Soccorsi

2016

Journal of Economic Dynamics and Control

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Abstract. As nonfundamental vector moving averages do not have causal VAR representations, standard structural VAR methods are deemed inappropriate for recovering the economic shocks of general equilibrium models with nonfundamental reduced forms. In previous literature it has been pointed out that, despite nonfundamentalness, structural VARs may still be good approximating models. I characterize nonfundamentalness as bias depending on the zeros of moving average lters. However, measuring the nonfundamental bias is not trivial because of the simultaneous occurrence of lag truncation bias. I propose a method to disentangle the bias based on population spectral density and derive a measure for the nonfundamental bias in population. In the application, I nd that the SVAR exercises of Sims (2012) are accurate because the nonfundamental bias is mild.

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Are spectral estimators useful for long-run restrictions in SVARs?

Cited by 6 publications

References 38 publications

Local Projections and VARs Estimate the Same Impulse Responses

Local Projections and VARs Estimate the Same Impulse Responses

On the Identification of Technology Shocks: An Alternative to the Standard Long-Run Method

Measuring nonfundamentalness for structural VARs

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