Structural Vector Autoregressions With Nonnormal Residuals
By Markku Lanne, H. Luetkepohl. Social Science Research Network, 2010
176 citations recorded by the source corpus.
In structural vector autoregressive (SVAR) modeling, sometimes the identifying restrictions are insufficient for a unique specification of all shocks. In this paper it is pointed out that specific distributional assumptions can help in identifying the structural shocks. In particular, a mixture of normal distributions is considered as a possible model that can be used in this context. Our model setup enables us to test restrictions which are just-identifying in a standard SVAR framework. The results are illustrated using a U.S. macro data set and a system of U.S. and European interest rates.
Subfields: Economics and Econometrics
View Paper: https://www.semanticscholar.org/paper/4be4b0a2a1e5d0b2cc3cfbb58160cb3419a40954
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In Economics and Econometrics
- Helmut Lütkepohl
- Pentti Saikkonen
- Markku Lanne
- Mika Meitz
- Katarzyna Maciejowska
- Muhammad Shahbaz
- Timo Teräsvirta
- Theodore Panagiotidis
Citations in the corpus
- Structural Vector Autoregressions with Markov Switching (2010, methodology): “The first question of interest is whether the MS model is preferable to the model with mixed normal residuals which was used by Lanne and Lütkepohl (2009).”
- Identification and estimation of non-Gaussian structural vector autoregressions (2015, background): “Lanne and Lütkepohl (2010) assume that the error term of the SVAR model follows a mixture of two Gaussian distributions, whereas our model allows for a wide variety of (non-Gaussian) distributions.”