Non-parametric estimation of time varying AR(1)–processes with local stationarity and periodicity

International audienceExtending the ideas of [7], this paper aims at providing a kernel based non-parametric estimation of a new class of time varying AR(1) processes (Xt), with local stationarity and periodic features (with a known period T), inducing the definition Xt = at(t/nT)X t−1 + ξt for t ∈ N and with a t+T ≡ at. Central limit theorems are established for kernel estima-tors as(u) reaching classical minimax rates and only requiring low order moment conditions of the white noise (ξt)t up to the second order