Liangjun Su, Thomas Tao Yang, Yonghui Zhang, Qiankun Zhou
arXiv 26 Apr 2022 · Econometrics
arXiv:2204.12023 · PDF · DOI · OpenAlex · Extracted main text
This paper proposes a one-covariate-at-a-time multiple testing (OCMT) approach to choose significant variables in high-dimensional nonparametric additive regression models. Similarly to Chudik, Kapetanios and Pesaran (2018), we consider the statistical significance of individual nonparametric additive components one at a time and take into account the multiple testing nature of the problem. One-stage and multiple-stage procedures are both considered. The former works well in terms of the true positive rate only if the marginal effects of all signals are strong enough; the latter helps to pick up hidden signals that have weak marginal effects. Simulations demonstrate the good finite sample performance of the proposed procedures. As an empirical application, we use the OCMT procedure on a dataset we extracted from the Longitudinal Survey on Rural Urban Migration in China. We find that our procedure works well in terms of the out-of-sample forecast root mean square errors, compared with competing methods.
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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 | Huang, Horowitz and Wei (2010) Variable Selection in Nonparametric Additive Models, \ | 0.971 | 12 | 5 | 92% |
| 2 | Chen (2007) Large Sample Sieve Estimation of Semi-nonparametric Models,\ | 0.737 | 3 | 2 | 100% |
| 3 | Chudik, Kapetanios and Pesaran (2018) A One Covariate at a Time, Multiple Testing Approach to Variable Selection in High-Dimensional Linear Regression Models,\ | 0.737 | 3 | 2 | 100% |
| 4 | Stone (1985) Additive Regression and Other Nonparametric Models,\ | 0.659 | 7 | 4 | 29% |
| 5 | Belloni, Chen, Chernozhukov, and Hansen (2012) \ Sparse Models and Methods for Optimal Instruments with an Application to Eminent Domain,\ | 0.644 | 2 | 2 | 100% |
| 6 | Fan, Feng and Song (2011) Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Additive Models,\ | 0.644 | 2 | 2 | 100% |
| 7 | de Boor (2001) A Practical Guide to Splines | 0.511 | 3 | 2 | 33% |
| 8 | Horowitz and Mammen (2004) Nonparametric Estimation of an Additive Model with a Link Function,\ | 0.511 | 2 | 1 | 100% |
| 9 | Kozbur (2020) Analysis of Testing-Based Forward Model Selection,\ | 0.511 | 2 | 1 | 100% |
| 10 | Zou (2006) The Adaptive Lasso and Its Oracle Properties,\ | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 43 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | High Dimensional Binary Choice Model with Unknown Heteroskedasticity or Instrumental Variables | 0.000 | 1 | 1 |