Victor Chernozhukov, Whitney Newey, Vira Semenova
arXiv 24 Aug 2019 · Statistics — Machine Learning · 2 citations (OpenAlex)
arXiv:1908.09173 · PDF · DOI · OpenAlex · Extracted main text
This paper introduces metrics for welfare analysis in dynamic models. We develop estimation and inference for these parameters even in the presence of a high-dimensional state space. Examples of welfare metrics include average welfare, average marginal welfare effects, and welfare decompositions into direct and indirect effects similar to Oaxaca (1973) and Blinder (1973). We derive dual and doubly robust representations of welfare metrics that facilitate debiased inference. For average welfare, the value function does not have to be estimated. In general, debiasing can be applied to any estimator of the value function, including neural nets, random forests, Lasso, boosting, and other high-dimensional methods. In particular, we derive Lasso and Neural Network estimators of the value function and associated dynamic dual representation and establish associated mean square convergence rates for these functions. Debiasing is automatic in the sense that it only requires knowledge of the welfare metric of interest, not the form of bias correction. The proposed methods are applied to estimate a dynamic behavioral model of teacher absenteeism in \cite{DHR} and associated average teacher welfare.
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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 | Duflo, E., R. Hanna, and S. P. Ryan (2012, June) (2012) Incentives work: Getting teachers to come to school | 0.982 | 19 | 8 | 95% |
| 2 | Hotz, J. and R. Miller (1993) Conditional choice probabilities and the estimation of dynamic models | 0.928 | 10 | 5 | 80% |
| 3 | Blinder, A. S (1973) Wage discrimination: Reduced form and structural estimates | 0.928 | 4 | 3 | 100% |
| 4 | Oaxaca, R (1973) Male-female wage differentials in urban labor markets | 0.928 | 4 | 3 | 100% |
| 5 | Rust, J (1987) Optimal replacement of gmc bus engines: An empirical model of harold zurcher | 0.909 | 8 | 6 | 75% |
| 6 | Aguirregabiria, V. and P. Mira (2002) Swapping the nested fixed point algorithm: A class of estimators for discrete markov decision models | 0.874 | 9 | 5 | 67% |
| 7 | Chernozhukov, V., W. K. Newey, and R. Singh (2022, May) (2022) Automatic debiased machine learning of causal and structural effects self | 0.855 | 8 | 4 | 62% |
| 8 | Adusumilli, K. and D. Eckardt (2019) Temporal-difference estimation of dynamic choice models | 0.843 | 5 | 3 | 60% |
| 9 | Chernozhukov, V., W. K. Newey, V. Quintas-Martinez, and V. Syrgkanis (2024) Automatic debiased machine learning via riesz regression self | 0.794 | 8 | 5 | 50% |
| 10 | Chernozhukov, V., J. C. Escanciano, H. Ichimura, W. K. Newey, and J.… (2022) Locally Robust Semiparametric Estimation self | 0.763 | 9 | 5 | 44% |
Showing the top 10 of 65 scored citations.