A Conditional-Heteroskedasticity-Robust Confidence Interval for the Autoregressive Parameter

34 Pages Posted: 1 Aug 2011 Last revised: 5 Aug 2011

See all articles by Donald W. K. Andrews

Donald W. K. Andrews

Yale University - Cowles Foundation

Patrik Guggenberger

Pennsylvania State University, College of the Liberal Arts - Department of Economic

Multiple version iconThere are 2 versions of this paper

Date Written: August 1, 2011

Abstract

This paper introduces a new confidence interval (CI) for the autoregressive parameter (AR) in an AR(1) model that allows for conditional heteroskedasticity of general form and AR parameters that are less than or equal to unity. The CI is a modification of Mikusheva's (2007a) modification of Stock's (1991) CI that employs the least squares estimator and a heteroskedasticity-robust variance estimator. The CI is shown to have correct asymptotic size and to be asymptotically similar (in a uniform sense). It does not require any tuning parameters. No existing procedures have these properties. Monte Carlo simulations show that the CI performs well in finite samples in terms of coverage probability and average length, for innovations with and without conditional heteroskedasticity.

Keywords: Asymptotically similar, Asymptotic size, Autoregressive model, Conditional heteroskedasticity, Confidence interval, Hybrid test, Subsampling test, Unit root

JEL Classification: C12, C15, C22

Suggested Citation

Andrews, Donald W. K. and Guggenberger, Patrik, A Conditional-Heteroskedasticity-Robust Confidence Interval for the Autoregressive Parameter (August 1, 2011). Cowles Foundation Discussion Paper No. 1812, Available at SSRN: https://ssrn.com/abstract=1899591 or http://dx.doi.org/10.2139/ssrn.1899591

Donald W. K. Andrews (Contact Author)

Yale University - Cowles Foundation ( email )

Box 208281
New Haven, CT 06520-8281
United States
203-432-3698 (Phone)
203-432-6167 (Fax)

Patrik Guggenberger

Pennsylvania State University, College of the Liberal Arts - Department of Economic ( email )

524 Kern Graduate Building
University Park, PA 16802-3306
United States

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