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Some Finite Sample Properties of the Sign Test

Yong Cai

arXiv 2 Mar 2021 · Econometrics

arXiv:2103.01412 · PDF · DOI · OpenAlex · Extracted main text

Abstract

This paper contains two finite-sample results concerning the sign test. First, we show that the sign-test is unbiased with independent, non-identically distributed data for both one-sided and two-sided hypotheses. The proof for the two-sided case is based on a novel argument that relates the derivatives of the power function to a regular bipartite graph. Unbiasedness then follows from the existence of perfect matchings on such graphs. Second, we provide a simple theoretical counterexample to show that the sign test over-rejects when the data exhibits correlation. Our results can be useful for understanding the properties of approximate randomization tests in settings with few clusters.

Citation extraction

15
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22
in-text mentions
15
distinct cited
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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Lehmann, E. and J. P. Romano (2005) Testing Statistical Hypotheses0.81142100%
2Hoeffding, W (1952) The Large-Sample Power of Tests Based on Permutations of Observations0.73732100%
3Bugni, F. A. and I. A. Canay (2021) Testing continuity of a density via g-order statistics in the regression discontinuity design0.51121100%
4Canay, I. A., J. P. Romano, and A. M. Shaikh (2017) Randomization Tests under an Approximate Symmetry Assumption0.51121100%
5Athey, S. and G. W. Imbens (2017) The State of Applied Econometrics: Causality and Policy Evaluation0.40511100%
6Bertrand, M., E. Duflo, and M. Sendhil (2004) How Much Should We Trust Differences-in-Differences Estimates?0.40511100%
7Cai, Y (2023) A modified randomization test for the level of clustering self0.40511100%
8Canay, I. A. and V. Kamat (2018) Approximate permutation tests and induced order statistics in the regression discontinuity design0.40511100%
9Cameron, A. C., J. B. Gelbach, and D. L. Miller (2008) Bootstrap-Based Improvements for Inference with Clustered Errors0.40511100%
10Corrado, C. J. and T. L. Zivney (1992) The specification and power of the sign test in event study hypothesis tests using daily stock returns0.40511100%

Showing the top 10 of 15 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Combining Clusters for the Approximate Randomization Test0.40511