Gianluca Cubadda, Francesco Giancaterini, Alain Hecq, Joann Jasiak
arXiv 26 Jun 2023 · Econometrics · publishedStatistics and Computing (2024) · 4 citations (OpenAlex)
arXiv:2306.14653 · PDF · DOI · OpenAlex · Extracted main text
This paper investigates the performance of the Generalized Covariance estimator (GCov) in estimating and identifying mixed causal and noncausal models. The GCov estimator is a semi-parametric method that minimizes an objective function without making any assumptions about the error distribution and is based on nonlinear autocovariances to identify the causal and noncausal orders. When the number and type of nonlinear autocovariances included in the objective function of a GCov estimator is insufficient/inadequate, or the error density is too close to the Gaussian, identification issues can arise. These issues result in local minima in the objective function, which correspond to parameter values associated with incorrect causal and noncausal orders. Then, depending on the starting point and the optimization algorithm employed, the algorithm can converge to a local minimum. The paper proposes the use of the Simulated Annealing (SA) optimization algorithm as an alternative to conventional numerical optimization methods. The results demonstrate that SA performs well when applied to mixed causal and noncausal models, successfully eliminating the effects of local minima. The proposed approach is illustrated by an empirical application involving a bivariate commodity price series.
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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., Jasiak, J (2022) Generalized covariance estimator self | 1.000 | 7 | 3 | 100% |
| 2 | Gourieroux, C., Jasiak, J (2022) Nonlinear forecasts and impulse responses for causal-noncausal (s) var models self | 0.928 | 4 | 3 | 100% |
| 3 | Lanne, M., Saikkonen, P (2011) Noncausal autoregressions for economic time series | 0.928 | 4 | 3 | 100% |
| 4 | Gourieroux, C., Jasiak, J (2017) Noncausal vector autoregressive process: Representation, identification and semi-parametric estimation self | 0.874 | 10 | 2 | 100% |
| 5 | Lanne, M., Saikkonen, P (2013) Noncausal vector autoregression | 0.811 | 4 | 2 | 100% |
| 6 | Davis, R.A., Song, L (2020) Noncausal vector ar processes with application to economic time series | 0.737 | 3 | 2 | 100% |
| 7 | Hecq, A., Lieb, L., Telg, S (2016) Identification of mixed causal-noncausal models in finite samples self | 0.737 | 3 | 2 | 100% |
| 8 | Breidt, F.J., Davis, R.A., Lh, K.S., Rosenblatt, M (1991) Maximum likelihood estimation for noncausal autoregressive processes | 0.644 | 4 | 1 | 100% |
| 9 | Hecq, A., Velasquez-Gaviria, D (2022) Spectral estimation for mixed causal-noncausal autoregressive models self | 0.644 | 2 | 2 | 100% |
| 10 | Kirkpatrick, S., Gelatt Jr, C.D., Vecchi, M.P (1983) Optimization by simulated annealing | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 36 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | 2504.18678 | 1.000 | 5 | 4 |
| 2 | Seasonality in Mixed Causal-Noncausal Processes | 0.843 | 3 | 3 |
| 3 | 2501.03945 | 0.405 | 1 | 1 |
| 4 | 2505.14911 | 0.405 | 1 | 1 |
| 5 | 2509.13492 | 0.405 | 1 | 1 |