Matias D. Cattaneo, Max H. Farrell, Michael Jansson, Ricardo Masini
arXiv 31 Dec 2022 · Econometrics · publishedJournal of Econometrics (2024) · 2 citations (OpenAlex)
arXiv:2301.00277 · PDF · DOI · OpenAlex · Extracted main text
The density weighted average derivative (DWAD) of a regression function is a canonical parameter of interest in economics. Classical first-order large sample distribution theory for kernel-based DWAD estimators relies on tuning parameter restrictions and model assumptions that imply an asymptotic linear representation of the point estimator. These conditions can be restrictive, and the resulting distributional approximation may not be representative of the actual sampling distribution of the statistic of interest. In particular, the approximation is not robust to bandwidth choice. Small bandwidth asymptotics offers an alternative, more general distributional approximation for kernel-based DWAD estimators that allows for, but does not require, asymptotic linearity. The resulting inference procedures based on small bandwidth asymptotics were found to exhibit superior finite sample performance in simulations, but no formal theory justifying that empirical success is available in the literature. Employing Edgeworth expansions, this paper shows that small bandwidth asymptotic approximations lead to inference procedures with higher-order distributional properties that are demonstrably superior to those of procedures based on asymptotic linear approximations.
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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 | Nishiyama and Robinson (2000) Edgeworth Expansions for Semiparametric Averaged Derivatives | 1.000 | 10 | 3 | 100% |
| 2 | Powell, Stock and Stoker (1989) Semiparametric Estimation of Index Coefficients | 1.000 | 8 | 4 | 100% |
| 3 | Cattaneo, Crump and Jansson (2014) aSmall Bandwidth Asymptotics for Density-Weighted Average Derivatives self | 0.969 | 11 | 4 | 91% |
| 4 | Nishiyama and Robinson (2001) Studentization in Edgeworth Expansions for Estimates of Semiparametric Index Models | 0.950 | 7 | 4 | 86% |
| 5 | Cattaneo, Crump and Jansson (2010) Robust Data-Driven Inference for Density-Weighted Average Derivatives self | 0.941 | 6 | 4 | 83% |
| 6 | Cattaneo, Crump and Jansson (2014) bBootstrapping Density-Weighted Average Derivatives self | 0.874 | 12 | 6 | 67% |
| 7 | Nishiyama and Robinson (2005) The Bootstrap and the Edgeworth Correction for Semiparametric Averaged Derivatives | 0.843 | 3 | 3 | 100% |
| 8 | Cattaneo, Crump and Jansson (2013) Generalized Jackknife Estimators of Weighted Average Derivatives (with Discussions and Rejoinder) self | 0.644 | 2 | 2 | 100% |
| 9 | Cattaneo, Jansson and Ma (2019) Two-step Estimation and Inference with Possibly Many Included Covariates | 0.644 | 2 | 2 | 100% |
| 10 | Cattaneo, Jansson and Newey (2018) Alternative Asymptotics and the Partially Linear Model with Many Regressors | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 41 scored citations.
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| Citing paper | Intensity | Mentions | Sections | |
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| 1 | Higher-Order Debiased Estimators for General Treatment Models | 0.928 | 5 | 4 |
| 2 | Robust Inference for Convex Pairwise Difference Estimators | 0.644 | 2 | 2 |
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