arXiv 9 Oct 2019 · Econometrics · 1 citations (OpenAlex)
arXiv:1910.04087 · PDF · DOI · OpenAlex · Extracted main text
This paper analyzes identifiability properties of structural vector autoregressive moving average (SVARMA) models driven by independent and non-Gaussian shocks. It is well known, that SVARMA models driven by Gaussian errors are not identified without imposing further identifying restrictions on the parameters. Even in reduced form and assuming stability and invertibility, vector autoregressive moving average models are in general not identified without requiring certain parameter matrices to be non-singular. Independence and non-Gaussianity of the shocks is used to show that they are identified up to permutations and scalings. In this way, typically imposed identifying restrictions are made testable. Furthermore, we introduce a maximum-likelihood estimator of the non-Gaussian SVARMA model which is consistent and asymptotically normally distributed.
appendix boundary found by appendix_command · 29% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Edward J. Hannan and Manfred Deistler (2012) The Statistical Theory of Linear Systems | 0.928 | 4 | 3 | 100% |
| 2 | Lutz Kilian and Helmut Lütkepohl (2017) Structural Vector Autoregressive Analysis | 0.811 | 4 | 2 | 100% |
| 3 | Markku Lanne, Mika Meitz, and Pentti Saikkonen (2016) Identification and estimation of non-gaussian structural vector autoregressions | 0.737 | 25 | 12 | 40% |
| 4 | Donald S. Poskitt and Wenying Yao (2015) Vector autoregressions and macroeconomic modeling: An error taxonomy | 0.737 | 3 | 2 | 100% |
| 5 | Helmut Lütkepohl (2005) New Introduction to Multiple Time Series Analysis | 0.644 | 2 | 2 | 100% |
| 6 | Federico Ravenna (2006) Vector autoregressions and reduced form representations of \ models | 0.585 | 3 | 1 | 100% |
| 7 | Christian Gourieroux, Alain Monfort, and Jean-Paul Renne (2019) Identification and Estimation in Non-Fundamental Structural VARMA Models | 0.511 | 2 | 1 | 100% |
| 8 | George Athanasopoulos and Farshid Vahid (2008) Varma versus var for macroeconomic forecasting | 0.405 | 1 | 1 | 100% |
| 9 | George Athanasopoulos and Farshid Vahid (2007) A complete varma modelling methodology based on scalar components | 0.405 | 1 | 1 | 100% |
| 10 | Yacouba Boubacar Mainassara and Christian Francq (2010) Estimating structural varma models with uncorrelated but non-independent error terms | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 38 scored citations.