Mixing convergence of LSE for supercritical AR(2) processes with Gaussian innovations using random scaling

Autor:	Barczy, Matyas, Nedényi, Fanni, Pap, Gyula
Rok vydání:	2021
Předmět:	Mathematics - Statistics Theory Mathematics - Probability 62F12 62H12 60G15 60F05
Druh dokumentu:	Working Paper
Popis:	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. Comment: 37 pages
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2101.01590 Zobrazit plný text záznamu View this record from Arxiv