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Distribution-free tests of conditional moment inequalities

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URI: https://hdl.handle.net/10016/25162
ISSN: 0378-3758
DOI: https://doi.org/10.1016/j.jspi.2015.12.005
UXXI: AR/0000017900
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distribution_delgado_JSPI_2016_ps.pdf (880.09 KB)
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2016-06-01
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Elsevier
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Abstract
This article proposes testing the hypothesis of a uniformly non-positive nonparametric regression function using a test statistic with tabulated critical values. The none hypothesis is characterized in terms of the significance of a parameter, which measures a distance from the double-integrated regression function to the class of concave functions. The test statistic is a suitably scaled parameter estimate, which does not require smooth estimation of the underlying regression and/or the conditional variance functions. The finite sample performance of the proposed test is studied by means of two Monte Carlo experiments, showing that the proposed method compares favorably to existing procedures. (C) 2015 Elsevier B.V. All rights reserved.
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Nonparametric testing, Conditional inequalities, Least concave majorant, Nonparametric regression, Order statistics
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Delgado, M. A., Escanciano, J. C. (2016). Distribution-free tests of conditional moment inequalities. Journal of Statistical Planning and Inference, v. 173, pp. 99-108.
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