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

Donald W. K. Andrews1; Patrik Guggenberger2

1 Cowles Foundation for Research in Economics, Yale University · 2 Pennsylvania State University

The Review of Economics and Statistics 2014

This paper introduces a new confidence interval (CI) for the autoregressive parameter (AR) in an AR(1) model that allows for conditional heteroskedasticity of a 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.

DOI
10.1162/rest_a_00369
Volume
96 (2)
Pages
376-381
Language
en
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