A Smoothed P-Value Test When There is a Nuisance Parameter under the Alternative

TL;DR

This paper introduces a new p-value occupation time (PVOT) test for hypotheses involving nuisance parameters, offering easy inference, robustness to various asymptotic conditions, and bootstrap applicability.

Contribution

The paper proposes the PVOT test that simplifies inference under nuisance parameters, accommodating weak identification and heavy tails without requiring root(n)-Gaussian asymptotics.

Findings

Asymptotic critical value upper bound equals significance level α.

PVOT controls for conservativeness and trivial power issues.

Enables bootstrap inference with nuisance parameters and potential non-identification.

Abstract

We present a new test when there is a nuisance parameter under the alternative hypothesis. The test exploits the p-value occupation time [PVOT], the measure of the nuisance parameter subset on which a p-value test based on a a test statistic rejects the null hypothesis. Key contributions are: (i) An asymptotic critical value upper bound for our test is the significance level {\alpha}, making inference easy. (ii) We only require the test statistic to have a known or bootstrappable limit distribution, hence we do not require root(n)-Gaussian asymptotics, allowing for weak or non-identification, boundary values, heavy tails, infill asymptotics, and so on. (iii) A test based on the sup-p-value may be conservative and in some cases have trivial power, while the PVOT naturally controls for this by smoothing over the nuisance parameter space. Finally, (iv) the PVOT uniquely allows for bootstrap inference in the presence of nuisance parameters when some estimated parameters may not be identified.