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Gaussian inference in general AR(1) models based on difference

Authors


Correspondence to: Biing-Shen Kuo, Department of International Business, National Chengchi University, Taipei 116, Taiwan.

E-mail: bsku@nccu.edu.tw

Abstract

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 inline image 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.

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