Confidence Sets in Regressions with Highly Serially Correlated Regressors

James Stock and Mark Watson
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James Stock: Harvard University
Mark Watson: Princeton University

Working Papers from Princeton University. Economics Department.

Abstract: Small deviations from exact unit roots can product large coverage rate distortions for conventional confidence sets for cointegrating coefficients (Elliott [1994]). We therefore propose new methods for constructing confidence sets for long-run coefficients with highly serially correlated regressors which do not necessarily have a unit root. Although the standard bootstrap is shown to be asymptotically invalid, a modified, valid bootstrap is developed. invariant confidence sets that are option (highest average accuracy) are obtained but are difficult to implement in practice. An approximately optimal invariant method is proposed; this works almost as well as the optimal method, at least for a single persistent regressor.

Keywords: Cointegration; Local to Unit Roots; Money Demand (search for similar items in EconPapers)
JEL-codes: C01 (search for similar items in EconPapers)
Date: 1996-12
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Persistent link: https://EconPapers.repec.org/RePEc:pri:econom:1996-1

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