Hugo Bodory, Martin Huber, Michael Lechner
arXiv 14 Dec 2022 · Econometrics · publishedComputational Economics (2023)
arXiv:2212.07379 · PDF · DOI · OpenAlex · Extracted main text
This paper investigates the finite sample performance of a range of parametric, semi-parametric, and non-parametric instrumental variable estimators when controlling for a fixed set of covariates to evaluate the local average treatment effect. Our simulation designs are based on empirical labor market data from the US and vary in several dimensions, including effect heterogeneity, instrument selectivity, instrument strength, outcome distribution, and sample size. Among the estimators and simulations considered, non-parametric estimation based on the random forest (a machine learner controlling for covariates in a data-driven way) performs competitive in terms of the average coverage rates of the (bootstrap-based) 95% confidence intervals, while also being relatively precise. Non-parametric kernel regression as well as certain versions of semi-parametric radius matching on the propensity score, pair matching on the covariates, and inverse probability weighting also have a decent coverage, but are less precise than the random forest-based method. In terms of the average root mean squared error of LATE estimation, kernel regression performs best, closely followed by the random forest method, which has the lowest average absolute bias.
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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 | Huber, Lechner, and Wunsch (2013) The performance of estimators based on the propensity score | 1.000 | 7 | 3 | 100% |
| 2 | Angrist and Evans (1998) Children and their parents labor supply: Evidence from exogeneous variation in family size | 0.843 | 3 | 3 | 100% |
| 3 | Bodory, Camponovo, Huber, and Lechner (2020) The Finite Sample Performance of Inference Methods for Propensity Score Matching and Weighting Estimators | 0.811 | 4 | 2 | 100% |
| 4 | Frölich (2007) Nonparametric IV estimation of local average treatment effects with covariates | 0.737 | 3 | 2 | 100% |
| 5 | Imbens and Wooldridge (2009) Recent Developments in the Econometrics of Program Evaluation | 0.737 | 3 | 2 | 100% |
| 6 | Lechner, Miquel, and Wunsch (2011) Long-run Effects of Public Sector Sponsored Training in West Germany self | 0.737 | 3 | 2 | 100% |
| 7 | Abadie (2003) Semiparametric instrumental Variable estimation of treatment response models | 0.644 | 2 | 2 | 100% |
| 8 | Angrist, Imbens, and Rubin (1996) Identification of Causal Effects using Instrumental Variables | 0.644 | 2 | 2 | 100% |
| 9 | Frölich, Huber, and Wiesenfarth (2017) The finite sample performance of semi- and nonparametric estimators for treatment effects and policy evaluation | 0.644 | 2 | 2 | 100% |
| 10 | Imbens and Angrist (1994) Identification and Estimation of Local Average Treatment Effects | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 49 scored citations.