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

Donald Andrews () and Patrik Guggenberger

The Review of Economics and Statistics, 2014, vol. 96, issue 2, 376-381

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 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. © 2014 The President and Fellows of Harvard College and the Massachusetts Institute of Technology.

Keywords: asymptotically similar; asymptotic size; autoregressive model; conditional heteroskedasticity; con dence interval; hybrid test; subsampling test; unit root (search for similar items in EconPapers)
JEL-codes: C12 C15 C22 (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (11)

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Related works:
Working Paper: A Conditional-Heteroskedasticity-Robust Confidence Interval for the Autoregressive Parameter (2012)
Working Paper: A Conditional-Heteroskedasticity-Robust Confidence Interval for the Autoregressive Parameter (2011)
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