Abstract
The time-inhomogeneous autoregressive model AR(1) is studied, which is the process of the form ${X_{n+1}}={\alpha _{n}}{X_{n}}+{\varepsilon _{n}}$, where ${\alpha _{n}}$ are constants, and ${\varepsilon _{n}}$ are independent random variables. Conditions on ${\alpha _{n}}$ and distributions of ${\varepsilon _{n}}$ are established that guarantee the geometric recurrence of the process. This result is applied to estimate the stability of n-steps transition probabilities for two autoregressive processes ${X^{(1)}}$ and ${X^{(2)}}$ assuming that both ${\alpha _{n}^{(i)}}$, $i\in \{1,2\}$, and distributions of ${\varepsilon _{n}^{(i)}}$, $i\in \{1,2\}$, are close enough.
Subject
Statistics, Probability and Uncertainty,Modeling and Simulation,Statistics and Probability
Cited by
2 articles.
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1. Polynomial Recurrence of Time-inhomogeneous Markov Chains;Austrian Journal of Statistics;2023-08-15
2. Geometric recurrence of inhomogeneous Gaussian autoregression process;Bulletin of Taras Shevchenko National University of Kyiv. Series: Physics and Mathematics;2023