arXiv 11 Feb 2020 · Econometrics · 1 citations (OpenAlex)
arXiv:2002.04346 · PDF · DOI · OpenAlex · Extracted main text
This article deals with parameterisation, identifiability, and maximum likelihood (ML) estimation of possibly non-invertible structural vector autoregressive moving average (SVARMA) models driven by independent and non-Gaussian shocks. In contrast to previous literature, the novel representation of the MA polynomial matrix using the Wiener-Hopf factorisation (WHF) focuses on the multivariate nature of the model, generates insights into its structure, and uses this structure for devising optimisation algorithms. In particular, it allows to parameterise the location of determinantal zeros inside and outside the unit circle, and it allows for MA zeros at zero, which can be interpreted as informational delays. This is highly relevant for data-driven evaluation of Dynamic Stochastic General Equilibrium (DSGE) models. Typically imposed identifying restrictions on the shock transmission matrix as well as on the determinantal root location are made testable. Furthermore, we provide low level conditions for asymptotic normality of the ML estimator and analytic expressions for the score and the information matrix. As application, we estimate the Blanchard and Quah model and show that our method provides further insights regarding non-invertibility using a standard macroeconometric model. These and further analyses are implemented in a well documented R-package.
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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 | Carlos Velasco (2020) Identification and estimation of structural varma models using higher order dynamics, 2020 | 1.000 | 25 | 4 | 100% |
| 2 | Christian Gouriéroux, Alain Monfort, and Jean-Paul Renne (2019) Identification and Estimation in Non-Fundamental Structural VARMA Models | 1.000 | 21 | 5 | 100% |
| 3 | Kung-Sik Chan and Lop-Hing Ho (2004) On the Unique Representation of non-Gaussian Multivariate Linear Processes | 1.000 | 14 | 3 | 100% |
| 4 | Edward J. Hannan and Manfred Deistler (2012) The Statistical Theory of Linear Systems | 1.000 | 7 | 3 | 100% |
| 5 | Keh-Shin Lii and Murray Rosenblatt (1996) Maximum Likelihood Estimation for Nongaussian Nonminimum Phase ARMA Sequences | 1.000 | 7 | 3 | 100% |
| 6 | Markku Lanne and Pentti Saikkonen (2013) Noncausal Vector Autoregression | 1.000 | 6 | 3 | 100% |
| 7 | Markku Lanne, Mika Meitz, and Pentti Saikkonen (2016) Identification and estimation of non-gaussian structural vector autoregressions | 1.000 | 5 | 4 | 100% |
| 8 | Edward J. Hannan (1970) Multiple Time Series | 0.928 | 4 | 3 | 100% |
| 9 | Olivier J Blanchard and Danny Quah (1989) The Dynamic Effects of Aggregate Demand and Supply Disturbances | 0.874 | 14 | 2 | 100% |
| 10 | Keh-Shin Lii and Murray Rosenblatt (1992) An approximate maximum likelihood estimation for non-Gaussian non-minimum phase moving average processes | 0.874 | 7 | 2 | 100% |
Showing the top 10 of 90 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Structural Analysis of Vector Autoregressive Models | 0.405 | 1 | 1 |
| 2 | Vector AutoRegressive Moving Average Models: A Review | 0.405 | 1 | 1 |