arXiv 7 May 2025 · Econometrics
arXiv:2505.04414 · PDF · DOI · OpenAlex · Extracted main text
Specification tests, such as Integrated Conditional Moment (ICM) and Kernel Conditional Moment (KCM) tests, are crucial for model validation but often lack power in finite samples. This paper proposes a novel framework to enhance specification test performance using Support Vector Machines (SVMs) for direction learning. We introduce two alternative SVM-based approaches: one maximizes the discrepancy between nonparametric and parametric classes, while the other maximizes the separation between residuals and the origin. Both approaches lead to a $t$-type test statistic that converges to a standard chi-square distribution under the null hypothesis. Our method is computationally efficient and capable of detecting any arbitrary alternative. Simulation studies demonstrate its superior performance compared to existing methods, particularly in large-dimensional settings.
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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.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Juan Carlos Escanciano (2024) A gaussian process approach to model checks | 0.874 | 5 | 2 | 100% |
| 2 | Krikamol Muandet, Wittawat Jitkrittum, and Jonas Kübler (2020) Kernel conditional moment test via maximum moment restriction | 0.811 | 4 | 2 | 100% |
| 3 | Herman J Bierens (1982) Consistent model specification tests | 0.644 | 2 | 2 | 100% |
| 4 | B Schölkopf (2002) Learning with kernels: support vector machines, regularization, optimization, and beyond | 0.511 | 2 | 1 | 100% |
| 5 | Miguel A Delgado, Manuel A Domńguez, and Pascal Lavergne (2006) Consistent tests of conditional moment restrictions | 0.405 | 1 | 1 | 100% |
| 6 | J Carlos Escanciano (2006) A consistent diagnostic test for regression models using projections | 0.405 | 1 | 1 | 100% |
| 7 | Arthur Gretton, Dino Sejdinovic, Heiko Strathmann, Sivaraman Balakri… (2012) Optimal kernel choice for large-scale two-sample tests | 0.405 | 1 | 1 | 100% |
| 8 | Simon Hediger, Loris Michel, and Jeffrey Näf (2022) On the use of random forest for two-sample testing | 0.405 | 1 | 1 | 100% |
| 9 | Ilmun Kim, Aaditya Ramdas, Aarti Singh, and Larry Wasserman (2021) Classification accuracy as a proxy for two-sample testing | 0.405 | 1 | 1 | 100% |
| 10 | M Lichman (2017) Uci machine learning repository. school of information and computer science, university of california, irvine, ca (2013) | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 18 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | A Consistent ICM-based $^2$ Specification Test | 0.405 | 1 | 1 |