Testing multiple inequality hypotheses: A smoothed indicator approach

This paper proposes a class of origin-smooth approximators of indicators underlying the sum-of-negative-part statistic for testing multiple inequalities. The need for simulation or bootstrap to obtain test critical values is thereby obviated. A simple procedure is enabled using fixed critical values...

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Bibliographic Details
Published in:Journal of econometrics 2014-01, Vol.178 (3), p.678-693
Main Authors: Chen, Le-Yu, Szroeter, Jerzy
Format: Article
Language:English
Subjects:
Approximation
Asymptotic exactness
Binding constraints
Bootstrap mechanism
Bootstrap method
Composite null
Covariance singularity
Econometrics
Economic theory
Estimation
Hypothesis testing
Indicator smoothing
Model testing
Monte Carlo simulation
Multiple inequalities
One-sided hypothesis
Probability
Probability distribution
Statistical methods
Stochastic processes
Studies
Test
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Summary:This paper proposes a class of origin-smooth approximators of indicators underlying the sum-of-negative-part statistic for testing multiple inequalities. The need for simulation or bootstrap to obtain test critical values is thereby obviated. A simple procedure is enabled using fixed critical values. The test is shown to have correct asymptotic size in the uniform sense that supremum finite-sample rejection probability over null-restricted data distributions tends asymptotically to nominal significance level. This applies under weak assumptions allowing for estimator covariance singularity. The test is unbiased for a wide class of local alternatives. A new theorem establishes directions in which the test is locally most powerful. The proposed procedure is compared with predominant existing tests in structure, theory and simulation.
ISSN:0304-4076
1872-6895
DOI:10.1016/j.jeconom.2013.10.004