Identification and estimation of non-Gaussian structural vector autoregressions
By Markku Lanne, Mika Meitz, Pentti Saikkonen. 2015
208 citations recorded by the source corpus.
Conventional structural vector autoregressive (SVAR) models with Gaussian errors are not identified, and additional identifying restrictions are typically imposed in applied work. We show that the Gaussian case is an exception in that a SVAR model whose error vector consists of independent non-Gaussian components is, without any additional restrictions, identified and leads to (essentially) unique impulse responses. We also introduce an identification scheme under which the maximum likelihood estimator of the non-Gaussian SVAR model is consistent and asymptotically normally distributed. As a consequence, additional economic identifying restrictions can be tested. In an empirical application, we find a negative impact of a contractionary monetary policy shock on financial markets, and clearly reject the commonly employed recursive identifying restrictions.
Subfields: Economics and Econometrics
View Paper: https://www.semanticscholar.org/paper/a84625a820e98ecd4edfa05a3c554c309f2c7694
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In Economics and Econometrics
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Citations in the corpus
- DYNOTEARS: Structure Learning from Time-Series Data (2020, background): “There is also an extensive literature on learning SVAR models in the econometrics and statistics literature (Demiralp and Hoover, 2003; Lanne et al., 2017; Reale and Wilson, 2001, 2002; Swanson and Granger, 1997; Tank et al., 2019).”
- Granger Causality: A Review and Recent Advances (2021, background): “Alternatively, C can be directly estimated via maximum likelihood (Lanne et al., 2017).”
- GMM Estimation of Non-Gaussian Structural Vector Autoregression (2019, unknown): “Recently, Lanne, Meitz, and Saikkonen (2017) showed that identification of B (up top permutation and scaling of its columns) can be reached when the error term ε t is serially uncorrelated, and its components are contemporaneously independent and at most one of them is Gaussian.”