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Identification based on higher moments

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  • Daniel Lewis

Abstract

Identification based on higher moments has drawn increasing theoretical attention and been widely adopted in empirical practice in macroeconometrics in the last two decades. This article reviews two parallel strands of the literature: identification strategies based on heteroskedasticity and strategies based on non-Gaussianity more generally. I outline the seminal identification results and discuss recent extensions, parametric and non-parametric implementations, and prominent empirical applications. I additionally describe key issues for the adoption of such strategies, including weak identification and interpretability of statistically identified structural shocks. I further outline key areas of ongoing research.

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  • Daniel Lewis, 2024. "Identification based on higher moments," CeMMAP working papers 03/24, Institute for Fiscal Studies.
    • Handle: RePEc:azt:cemmap:03/24
    DOI: 10.47004/wp.cem.2023.0324
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    References listed on IDEAS

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    1. Davis, Richard & Ng, Serena, 2023. "Time series estimation of the dynamic effects of disaster-type shocks," Journal of Econometrics, Elsevier, vol. 235(1), pages 180-201.
    2. Emanuele Bacchiocchi & Luca Fanelli, 2015. "Identification in Structural Vector Autoregressive Models with Structural Changes, with an Application to US Monetary Policy," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(6), pages 761-779, December.
    3. Milunovich George & Yang Minxian, 2013. "On Identifying Structural VAR Models via ARCH Effects," Journal of Time Series Econometrics, De Gruyter, vol. 5(2), pages 117-131, May.
    4. Sascha Alexander Keweloh, 2021. "A Generalized Method of Moments Estimator for Structural Vector Autoregressions Based on Higher Moments," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(3), pages 772-782, July.
    5. Markku Lanne & Jani Luoto, 2020. "Identification of Economic Shocks by Inequality Constraints in Bayesian Structural Vector Autoregression," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 82(2), pages 425-452, April.
    6. José Luis Montiel Olea & Carolin Pflueger, 2013. "A Robust Test for Weak Instruments," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(3), pages 358-369, July.
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