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Gaussian inference in general AR(1) models based on difference
Author(s) -
Chen JhihGang,
Kuo BiingShen
Publication year - 2013
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/jtsa.12031
Subject(s) - mathematics , inference , limit (mathematics) , gaussian , transformation (genetics) , rate of convergence , convergence (economics) , gaussian process , econometrics , unit root , mathematical analysis , key (lock) , computer science , artificial intelligence , biochemistry , chemistry , physics , computer security , quantum mechanics , economics , gene , economic growth
This article develops a simple difference transformation for estimation and inference in general AR(1) models. As in Paparoditis and Politis (2000, Test 9, 487–509) and Phillips and Han (2008, Econometric Theory 24, 631–650), a Gaussian limit theory with a convergence rate of T is available, whether a unit root is present in the process. Yet the novelty of our limit results is that the same weak convergence applies to the models with or without a trend, unlike those established in the literature. The merits promise usefulness of the difference transformation in applications to dynamic panels.