Statistical Inference for Stochastic Processes, volume 26, issue 3, pages 619-641
Consistency and asymptotic normality in a class of nearly unstable processes
Marie Badreau
1
,
Frédéric Proïa
2
1
Laboratoire Manceau de Mathématiques, Le Mans Université, Le Mans Cedex 09, France
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2
CNRS, LAREMA, SFR MATHSTIC, Univ Angers, Angers, France
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Publication type: Journal Article
Publication date: 2023-06-02
scimago Q3
SJR: 0.363
CiteScore: 1.3
Impact factor: 0.7
ISSN: 13870874, 15729311
Statistics and Probability
Abstract
This paper deals with inference in a class of stable but nearly-unstable processes. Autoregressive processes are considered, in which the bridge between stability and instability is expressed by a time-varying companion matrix $$A_{n}$$ with spectral radius $$\rho (A_{n}) < 1$$ satisfying $$\rho (A_{n}) \rightarrow 1$$ . This framework is particularly suitable to understand unit root issues by focusing on the inner boundary of the unit circle. Consistency is established for the empirical covariance and the OLS estimation together with asymptotic normality under appropriate hypotheses when A, the limit of $$A_n$$ , has a real spectrum, and a particular case is deduced when A also contains complex eigenvalues. The asymptotic process is integrated with either one unit root (located at 1 or $$-1$$ ), or even two unit roots located at 1 and $$-1$$ . Finally, a set of simulations illustrate the asymptotic behavior of the OLS. The results are essentially proved by $$L^2$$ computations and the limit theory of triangular arrays of martingales.
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