Do we reject restrictions identifying fiscal shocks? identification based on non-Gaussian innovations

Karamysheva, Madina; Skrobotov, Anton

doi:10.1016/j.jedc.2022.104358

Cited by 5 publications

(1 citation statement)

References 39 publications

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“…In non-Gaussian structural vector autoregressions (SVAR), higher-order moment conditions derived from mutually independent structural shocks can be used to identify the simultaneous relationship. These higher-order moment conditions can be used to estimate the SVAR with a generalized method of moments (GMM) estimator or similar moment-based estimators, see, e.g., Lanne and Luoto (2021), Keweloh (2021), Guay (2021), Mesters and Zwiernik (2022), Amengual et al (2022), Karamysheva and Skrobotov (2022), Lanne and Luoto (2022), Lanne et al (2022), Keweloh et al (2023), or Drautzburg and Wright (2023).…”

Section: Introductionmentioning

confidence: 99%

A Generalized Method of Moments Estimator for Structural Vector Autoregressions Based on Higher Moments

Keweloh

2020

Journal of Business & Economic Statistics

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I propose a generalized method of moments estimator for structural vector autoregressions with independent and non-Gaussian shocks. The shocks are identified by exploiting information contained in higher moments of the data. Extending the standard identification approach, which relies on the covariance, to the coskewness and cokurtosis allows to identify and estimate the simultaneous interaction without any further restrictions. I analyze the finite sample properties of the estimator and apply it to illustrate the simultaneous interaction between economic activity, oil and stock prices.

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