arXiv 13 Sep 2021 · Econometrics · 5 citations (OpenAlex)
arXiv:2109.06150 · PDF · DOI · OpenAlex · Extracted main text
Truncated conditional expectation functions are objects of interest in a wide range of economic applications, including income inequality measurement, financial risk management, and impact evaluation. They typically involve truncating the outcome variable above or below certain quantiles of its conditional distribution. In this paper, based on local linear methods, a novel, two-stage, nonparametric estimator of such functions is proposed. In this estimation problem, the conditional quantile function is a nuisance parameter that has to be estimated in the first stage. The proposed estimator is insensitive to the first-stage estimation error owing to the use of a Neyman-orthogonal moment in the second stage. This construction ensures that inference methods developed for the standard nonparametric regression can be readily adapted to conduct inference on truncated conditional expectations. As an extension, estimation with an estimated truncation quantile level is considered. The proposed estimator is applied in two empirical settings: sharp regression discontinuity designs with a manipulated running variable and randomized experiments with sample selection.
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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 | Lee, D. S (2009) Training, wages, and sample selection: Estimating sharp bounds on treatment effects | 1.000 | 9 | 3 | 100% |
| 2 | Gerard, F., Rokkanen, M., and Rothe, C (2020) Bounds on treatment effects in regression discontinuity designs with a manipulated running variable | 0.874 | 14 | 2 | 100% |
| 3 | Armstrong, T. B. and Kolesár, M (2020) Simple and honest confidence intervals in nonparametric regression | 0.874 | 6 | 3 | 67% |
| 4 | Linton, O. and Xiao, Z (2013) Estimation of and inference about the expected shortfall for time series with infinite variance | 0.737 | 3 | 2 | 100% |
| 5 | Dimitriadis, T., Bayer, S., et al (2019) A joint quantile and expected shortfall regression framework | 0.644 | 3 | 2 | 67% |
| 6 | Semenova, V (2020) Better Lee bounds | 0.644 | 2 | 2 | 100% |
| 7 | Shorack, G. R. et al (1974) Random means | 0.644 | 2 | 2 | 100% |
| 8 | Kato, K (2012) Weighted Nadaraya–Watson estimation of conditional expected shortfall | 0.585 | 10 | 4 | 20% |
| 9 | Ruppert, D. and Carroll, R. J (1980) Trimmed least squares estimation in the linear model | 0.511 | 2 | 2 | 50% |
| 10 | Jones, M. C (1993) Simple boundary correction for kernel density estimation | 0.511 | 2 | 2 | 50% |
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