Non-parametric estimation of time varying AR(1)--processes with local stationarity and periodicity Article Swipe
Related Concepts
Minimax
Mathematics
Parametric statistics
Limit (mathematics)
Moment (physics)
Kernel (algebra)
White noise
Applied mathematics
Nonparametric statistics
Order (exchange)
Estimation
Class (philosophy)
Local time
Noise (video)
Combinatorics
Econometrics
Mathematical analysis
Statistics
Mathematical economics
Computer science
Physics
Economics
Artificial intelligence
Image (mathematics)
Classical mechanics
Finance
Management
Jean‐Marc Bardet
,
Paul Doukhan
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1705.10140
· OA: W2951121744
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1705.10140
· OA: W2951121744
Extending 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 + $ξ$t for t $\in$ N and with a t+T $\not\equiv$ 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 ($ξ$t)t up to the second order.
Related Topics
Finding more related topics…