Krikamol Muandet, Wittawat Jitkrittum, Jonas Kübler
arXiv 21 Feb 2020 · Mathematics — Statistics Theory · 6 citations (OpenAlex)
arXiv:2002.09225 · PDF · DOI · OpenAlex · Extracted main text
We propose a new family of specification tests called kernel conditional moment (KCM) tests. Our tests are built on a novel representation of conditional moment restrictions in a reproducing kernel Hilbert space (RKHS) called conditional moment embedding (CMME). After transforming the conditional moment restrictions into a continuum of unconditional counterparts, the test statistic is defined as the maximum moment restriction (MMR) within the unit ball of the RKHS. We show that the MMR not only fully characterizes the original conditional moment restrictions, leading to consistency in both hypothesis testing and parameter estimation, but also has an analytic expression that is easy to compute as well as closed-form asymptotic distributions. Our empirical studies show that the KCM test has a promising finite-sample performance compared to existing tests.
appendix boundary found by appendix_command · 66% of the source is main text. Read the extracted text to check this.
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 | M. Delgado, M. Domínguez, and P. Lavergne (2006) Consistent tests of conditional moment restrictions | 1.000 | 7 | 3 | 100% |
| 2 | Q. Liu, J. Lee, and M. Jordan (2016) A kernelized Stein discrepancy for goodness-of-fit tests | 1.000 | 7 | 3 | 100% |
| 3 | H. Bierens (1982) Consistent model specification tests | 1.000 | 6 | 3 | 100% |
| 4 | K. Chwialkowski, H. Strathmann, and A. Gretton (2016) A kernel test of goodness of fit | 1.000 | 5 | 3 | 100% |
| 5 | A. Hall (2005) Generalized Method of Moments | 0.941 | 6 | 4 | 83% |
| 6 | I. Steinwart and A. Christmann (2008) Support Vector Machines | 0.928 | 5 | 3 | 80% |
| 7 | A. Bennett, N. Kallus, and T. Schnabel (2019) Deep generalized method of moments for instrumental variable analysis | 0.794 | 6 | 3 | 50% |
| 8 | G. Lewis and V. Syrgkanis (2018) Adversarial generalized method of moments | 0.794 | 6 | 3 | 50% |
| 9 | M. Carrasco and J.-P. Florens (2000) Generalization of GMM to a continuum of moment conditions | 0.737 | 3 | 3 | 67% |
| 10 | K. Muandet, K. Fukumizu, B. Sriperumbudur, and B. Schölkopf (2017) Kernel mean embedding of distributions: A review and beyond | 0.737 | 3 | 3 | 67% |
Showing the top 10 of 56 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.