arXiv 25 Jan 2025 · Econometrics
arXiv:2501.15307 · PDF · DOI · OpenAlex · Extracted main text
We propose a direct approach to calculating influence functions based on the concept of functional derivatives. The relative simplicity of our direct method is demonstrated through well-known examples. Using influence functions as a key device, we examine the connection and difference between local robustness and efficiency in both joint and sequential identification/estimation procedures. We show that the joint procedure is associated with efficiency, while the sequential procedure is linked to local robustness. Furthermore, we provide conditions that are theoretically verifiable and empirically testable on when efficient and locally robust estimation for the parameter of interest in a semiparametric model can be achieved simultaneously. In addition, we present straightforward conditions for an adaptive procedure in the presence of nuisance parameters.
appendix boundary found by appendix_command · 73% of the source is main text. Read the extracted text to check this.
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 | Ichimura, H. and W. K. Newey (2022) The Influence Function of Semiparametric Estimators | 1.000 | 7 | 4 | 100% |
| 2 | Newey, W. K (1994) The Asymptotic Variance of Semiparametric Estimators | 1.000 | 6 | 4 | 100% |
| 3 | Ackerberg, D., X. Chen, J. Hahn, and Z. Liao (2014) Asymptotic Efficiency of Semiparametric Two-step GMM | 1.000 | 6 | 3 | 100% |
| 4 | Hahn, J. and G. Ridder (2013) Asymptotic variance of semiparametric estimators with generated regressors | 1.000 | 5 | 4 | 100% |
| 5 | Chernozhukov, V., J. C. Escanciano, H. Ichimura, W. K. Newey, and J.… (2022) Locally Robust Semiparametric Estimation | 1.000 | 5 | 3 | 100% |
| 6 | Bickel, P. J., C. A. Klaassen, Y. Ritov, and J. A. Wellner (1993) Efficient and Adaptive Estimation for Semiparametric Models | 0.909 | 8 | 3 | 75% |
| 7 | Hahn, J (1998) On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects | 0.843 | 5 | 3 | 60% |
| 8 | Huber, P. J (1984) Robust Statistics | 0.644 | 3 | 2 | 67% |
| 9 | Stein, C (1956) Efficient nonparametric testing and estimation, in | 0.644 | 2 | 2 | 100% |
| 10 | Tsiatis, A. A (2006) Semiparametric theory and missing data | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 38 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Distributionally Robust Treatment Effect | 0.000 | 1 | 1 |