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Learning Before Testing: A Selective Nonparametric Test for Conditional Moment Restrictions

Jia Li et al.

The Review of Economics and Statistics2026https://doi.org/10.1162/rest.a.1696article
AJG 4ABDC A*
Weight
0.50

What the paper says

We develop a new test for conditional moment restrictions via nonparametric series regression, with approximating functions selected by Lasso. A key novelty of our approach is to account for the effect of the data-driven selection, yielding a new critical value constructed on the basis of a nonstandard truncated-Gaussian asymptotic approximation. We show that the test is correctly sized and attains a well-defined sense of adaptiveness that may result in better power than existing methods. The improvement afforded by the new test is demonstrated in a Monte Carlo study and an empirical application on the conditional evaluation of inflation forecasts.

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https://doi.org/https://doi.org/10.1162/rest.a.1696

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@article{jia2026,
  title        = {{Learning Before Testing: A Selective Nonparametric Test for Conditional Moment Restrictions}},
  author       = {Jia Li et al.},
  journal      = {The Review of Economics and Statistics},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1162/rest.a.1696},
}

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Learning Before Testing: A Selective Nonparametric Test for Conditional Moment Restrictions

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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