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Nearly Optimal Tests When A Nuisance Parameter Is Present Under The Null Hypothesis

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  • Elliott, Graham
  • Mueller, Ulrich K
  • Watson, Mark W

Abstract

This paper considers nonstandard hypothesis testing problems that involve a nuisance parameter. We establish an upper bound on the weighted average power of all valid tests, and develop a numerical algorithm that determines a feasible test with power close to the bound. The approach is illustrated in six applications: inference about a linear regression coefficient when the sign of a control coefficient is known; small sample inference about the difference in means from two independent Gaussian samples from populations with potentially different variances; inference about the break date in structural break models with moderate break magnitude; predictability tests when the regressor is highly persistent; inference about an interval identified parameter; and inference about a linear regression coefficient when the necessity of a control is in doubt.
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Suggested Citation

  • Elliott, Graham & Mueller, Ulrich K & Watson, Mark W, 2015. "Nearly Optimal Tests When A Nuisance Parameter Is Present Under The Null Hypothesis," University of California at San Diego, Economics Working Paper Series qt5jp0q0fx, Department of Economics, UC San Diego.
  • Handle: RePEc:cdl:ucsdec:qt5jp0q0fx
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