Consistency and asymptotic normality in a class of nearly unstable processes - Archive ouverte HAL
Article Dans Une Revue Statistical Inference for Stochastic Processes Année : 2023

Consistency and asymptotic normality in a class of nearly unstable processes

Résumé

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 An with spectral radius ρ(An)<1 satisfying ρ(An)→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 An, 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 L2 computations and the limit theory of triangular arrays of martingales.

Dates et versions

hal-04233891 , version 1 (09-10-2023)

Identifiants

Citer

Marie Badreau, Frédéric Proïa. Consistency and asymptotic normality in a class of nearly unstable processes. Statistical Inference for Stochastic Processes, 2023, 26 (3), pp.619-641. ⟨10.1007/s11203-023-09290-2⟩. ⟨hal-04233891⟩
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