arXiv 31 Oct 2018 · Econometrics
arXiv:1811.00157 · PDF · DOI · OpenAlex · Extracted main text
Partial mean with generated regressors arises in several econometric problems, such as the distribution of potential outcomes with continuous treatments and the quantile structural function in a nonseparable triangular model. This paper proposes a nonparametric estimator for the partial mean process, where the second step consists of a kernel regression on regressors that are estimated in the first step. The main contribution is a uniform expansion that characterizes in detail how the estimation error associated with the generated regressor affects the limiting distribution of the marginal integration estimator. The general results are illustrated with two examples: the generalized propensity score for a continuous treatment (Hirano and Imbens, 2004) and control variables in triangular models (Newey, Powell, and Vella, 1999; Imbens and Newey, 2009). An empirical application to the Job Corps program evaluation demonstrates the usefulness of the method.
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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 | Imbens, G. W. and W. K. Newey (2009) Identification and estimation of triangular simultaneous equations models without additivity | 1.000 | 12 | 4 | 100% |
| 2 | Newey, W. K., J. L. Powell, and F. Vella (1999) Nonparametric estimation of triangular simultaneous equations models | 1.000 | 7 | 3 | 100% |
| 3 | Flores, C. A., A. Flores-Lagunes, A. Gonzalez, and T. C. Neumann (2012) Estimating the effects of length of exposure to instruction in a training program: The case of job corps | 1.000 | 6 | 3 | 100% |
| 4 | Hirano, K. and G. W. Imbens (2004) The propensity score with continuous treatments | 0.950 | 7 | 4 | 86% |
| 5 | Mammen, E., C. Rothe, and M. Schienle (2016) Semiparametric estimation with generated covariates | 0.941 | 6 | 4 | 83% |
| 6 | Hahn, J. and G. Ridder (2016) Three-stage semi-parametric inference: Control variables and differentiability | 0.928 | 5 | 4 | 80% |
| 7 | Newey, W. K (1994) Kernel estimation of partial means and a general variance estimator | 0.928 | 5 | 4 | 80% |
| 8 | Mammen, E., C. Rothe, and M. Schienle (2012) Nonparametric regression with nonparametrically generated covariates | 0.894 | 7 | 3 | 71% |
| 9 | Vanhems, A. and I. van Keilegom (2018) Semiparametric transformation model with endogeneity: a control function approach | 0.843 | 4 | 4 | 75% |
| 10 | Su, L. and A. Ullah (2008) Local polynomial estimation of nonparametric simultaneous equations models | 0.843 | 4 | 3 | 75% |
Showing the top 10 of 61 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments | 0.928 | 5 | 4 |
| 2 | 2106.04237 | 0.874 | 5 | 2 |
| 3 | Doubly Robust Inference on Causal Derivative Effects for Continuous Treatments | 0.644 | 2 | 2 |
| 4 | Estimation of Conditional Average Treatment Effects with High-Dimensional Data | 0.585 | 3 | 1 |
| 5 | Lee Bounds with a Continuous Treatment in Sample Selection | 0.405 | 1 | 1 |