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Confidence Sets in Regressions with Highly Serially Correlated Regressors

Author

Listed:
  • James H. Stock

    (Harvard University)

  • Mark W. Watson

    (Princeton University)

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.

Suggested Citation

  • James H. Stock & Mark W. Watson, 1996. "Confidence Sets in Regressions with Highly Serially Correlated Regressors," Working Papers 1996-1, Princeton University. Economics Department..
    • Handle: RePEc:pri:econom:1996-1
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    File URL: http://www.princeton.edu/~mwatson/papers/boot5.pdf
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    More about this item

    Keywords

    Cointegration; Local to Unit Roots; Money Demand;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics

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