Christian Gourieroux, Joann Jasiak
arXiv 14 Jul 2021 · Econometrics · publishedJournal of Business and Economic Statistics (2022) · 10 citations (OpenAlex)
arXiv:2107.06979 · PDF · DOI · OpenAlex · Extracted main text
We consider a class of semi-parametric dynamic models with strong white noise errors. This class of processes includes the standard Vector Autoregressive (VAR) model, the nonfundamental structural VAR, the mixed causal-noncausal models, as well as nonlinear dynamic models such as the (multivariate) ARCH-M model. For estimation of processes in this class, we propose the Generalized Covariance (GCov) estimator, which is obtained by minimizing a residual-based multivariate portmanteau statistic as an alternative to the Generalized Method of Moments. We derive the asymptotic properties of the GCov estimator and of the associated residual-based portmanteau statistic. Moreover, we show that the GCov estimators are semi-parametrically efficient and the residual-based portmanteau statistics are asymptotically chi-square distributed. The finite sample performance of the GCov estimator is illustrated in a simulation study. The estimator is also applied to a dynamic model of cryptocurrency prices.
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arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Optimization of the Generalized Covariance Estimator in Noncausal Processes | 1.000 | 7 | 3 |
| 2 | 2607.28131 | 0.874 | 5 | 2 |
| 3 | 2509.13492 | 0.855 | 16 | 5 |
| 4 | 2504.18678 | 0.817 | 11 | 5 |
| 5 | 2505.14911 | 0.693 | 5 | 1 |
| 6 | Nonlinear Forecast Error Variance Decompositions with Hermite Polynomials | 0.511 | 2 | 2 |
| 7 | 2603.10152 | 0.511 | 2 | 1 |
| 8 | 2501.03945 | 0.405 | 1 | 1 |
| 9 | Seasonality in Mixed Causal-Noncausal Processes | 0.405 | 1 | 1 |