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Adaptive estimation in partially linear autoregressive models
Author(s) -
Gao Jiti,
Yee Thomas
Publication year - 2000
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.2307/3315966
Subject(s) - autoregressive model , nonparametric statistics , kernel density estimation , star model , kernel (algebra) , parametric model , parametric statistics , linear model , mathematics , estimation , nonlinear autoregressive exogenous model , econometrics , computer science , mathematical optimization , statistics , time series , autoregressive integrated moving average , estimator , engineering , systems engineering , combinatorics
The authors consider a partially linear autoregressive model and construct kernel‐based estimates for both the parametric and nonparametric components. They propose an estimation procedure for the model and illustrate it through simulated and real data. Their work shows that the proposed estimation procedure not only has good asymptotic properties but also works well numerically. It also suggests that a partially linear autoregression is more appropriate than a completely nonparametric autoregression for some sets of data.