Mixing convergence of LSE for supercritical AR(2) processes with Gaussian innovations using random scaling
We prove mixing convergence of the least squares estimator of autoregressive parameters for supercritical autoregressive processes of order 2 with Gaussian innovations having real characteristic roots with different absolute values. We use an appropriate random scaling such that the limit distributi...
Elmentve itt :
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| Dokumentumtípus: | Cikk |
| Megjelent: |
2024
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| Sorozat: | METRIKA
87 No. 6 |
| Tárgyszavak: | |
| doi: | 10.1007/s00184-023-00936-y |
| mtmt: | 34675847 |
| Online Access: | http://publicatio.bibl.u-szeged.hu/36716 |
| Tartalmi kivonat: | We prove mixing convergence of the least squares estimator of autoregressive parameters for supercritical autoregressive processes of order 2 with Gaussian innovations having real characteristic roots with different absolute values. We use an appropriate random scaling such that the limit distribution is a two-dimensional normal distribution concentrated on a one-dimensional ray determined by the characteristic root having the larger absolute value. |
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| Terjedelem/Fizikai jellemzők: | 729-756 |
| ISSN: | 0026-1335 |