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On inference for a semiparametric partially linear regression model with serially correlated errors
Author(s) -
You Jinhong,
Chen Gemai
Publication year - 2007
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 - 10.1002/cjs.5550350404
Subject(s) - semiparametric regression , semiparametric model , estimator , covariate , parametric statistics , inference , nonparametric statistics , model selection , computer science , mathematics , goodness of fit , statistics , component (thermodynamics) , econometrics , artificial intelligence , physics , thermodynamics
The authors consider a semiparametric partially linear regression model with serially correlated errors. They propose a new way of estimating the error structure which has the advantage that it does not involve any nonparametric estimation. This allows them to develop an inference procedure consisting of a bandwidth selection method, an efficient semiparametric generalized least squares estimator of the parametric component, a goodness‐of‐fit test based on the bootstrap, and a technique for selecting significant covariates in the parametric component. They assess their approach through simulation studies and illustrate it with a concrete application.