Tests of stochastic monotonicity with improved power

We develop improved statistical procedures for testing stochastic monotonicity. While existing tests use a fixed critical value to set the limiting rejection rate equal to nominal size at the least favorable case, we use a bootstrap procedure to raise the limiting rejection rate to nominal size over...

Full description

Saved in:
Bibliographic Details
Published inJournal of econometrics Vol. 207; no. 1; pp. 53 - 70
Main Author Seo, Juwon
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier B.V 01.11.2018
Elsevier Sequoia S.A
Subjects
Bootstrap method
Copula
Differential equations
Hadamard differentiability
Hadamard directional differentiability
Least concave majorant
Power
Statistical analysis
Stochastic models
Stochastic monotonicity
Hadamard directional differentiability
Copula
Stochastic monotonicity
Least concave majorant
Hadamard differentiability
C15
C14
Online AccessGet full text

Cover

More Information
Summary:We develop improved statistical procedures for testing stochastic monotonicity. While existing tests use a fixed critical value to set the limiting rejection rate equal to nominal size at the least favorable case, we use a bootstrap procedure to raise the limiting rejection rate to nominal size over much of the null. This improves power against relevant local alternatives. To show the validity of our approach we draw on recent results on the directional differentiability of the least concave majorant operator, and on bootstrap inference when smoothness conditions sufficient to apply the functional delta method for the bootstrap are not satisfied.
ISSN:0304-4076
1872-6895
DOI:10.1016/j.jeconom.2018.04.004