Francesco Giancaterini, Alain Hecq, Joann Jasiak, Aryan Manafi Neyazi
arXiv 25 Apr 2025 · Econometrics · publishedEconometrics Journal (2026)
arXiv:2504.18678 · PDF · DOI · OpenAlex · Extracted main text
We introduce a regularized Generalized Covariance (RGCov) estimator as an extension of the GCov estimator to high dimensional setting that results either from high-dimensional data or a large number of nonlinear transformations used in the objective function. The approach relies on a ridge-type regularization for high-dimensional matrix inversion in the objective function of the GCov. The RGCov estimator is consistent and asymptotically normally distributed. We provide the conditions under which it can reach semiparametric efficiency and discuss the selection of the optimal regularization parameter. We also examine the diagonal GCov estimator, which simplifies the computation of the objective function. The GCov-based specification test, and the test for nonlinear serial dependence (NLSD) are extended to the regularized RGCov specification and RNLSD tests with asymptotic Chi-square distributions. Simulation studies show that the RGCov estimator and the regularized tests perform well in the high dimensional setting. We apply the RGCov to estimate the mixed causal and noncausal VAR model of stock prices of green energy companies.
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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 | Gourieroux, C. and J. Jasiak (2017) Noncausal vector autoregressive process: Representation, identification and semi-parametric estimation | 1.000 | 9 | 4 | 100% |
| 2 | Cubadda, G., F. Giancaterini, A. Hecq, and J. Jasiak (2024) Optimization of the generalized covariance estimator in noncausal processes | 1.000 | 5 | 4 | 100% |
| 3 | Jasiak, J. and A. M. Neyazi (2023) Gcov-based portmanteau test self | 0.928 | 4 | 3 | 100% |
| 4 | Gourieroux, C. and J. Jasiak (2023) Generalized covariance estimator | 0.817 | 11 | 5 | 55% |
| 5 | Hall, M. K. and J. Jasiak (2024) Modelling common bubbles in cryptocurrency prices | 0.737 | 3 | 2 | 100% |
| 6 | Chan, K.-S., L.-H. Ho, and H. Tong (2006) A note on time-reversibility of multivariate linear processes | 0.644 | 2 | 2 | 100% |
| 7 | Cubadda, G. and A. Hecq (2011) Testing for common autocorrelation in data-rich environments | 0.644 | 2 | 2 | 100% |
| 8 | Cubadda, G., A. Hecq, and S. Telg (2019) Detecting co-movements in non-causal time series | 0.644 | 2 | 2 | 100% |
| 9 | Cubadda, G., A. Hecq, and E. Voisin (2023) Detecting common bubbles in multivariate mixed causal–noncausal models | 0.644 | 2 | 2 | 100% |
| 10 | Gourieroux, C. and J. Jasiak (2016) Filtering, prediction and simulation methods for noncausal processes | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 32 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
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| 1 | 2603.10152 | 0.811 | 4 | 2 |
| 2 | 2505.14911 | 0.405 | 1 | 1 |
| 3 | 2509.13492 | 0.405 | 1 | 1 |