arXiv 29 Oct 2024 · Econometrics
arXiv:2410.22574 · PDF · DOI · OpenAlex · Extracted main text
I consider inference in a partially linear regression model under stationary $\beta$-mixing data after first stage deep neural network (DNN) estimation. Using the DNN results of Brown (2024), I show that the estimator for the finite dimensional parameter, constructed using DNN-estimated nuisance components, achieves $\sqrt{n}$-consistency and asymptotic normality. By avoiding sample splitting, I address one of the key challenges in applying machine learning techniques to econometric models with dependent data. In a future version of this work, I plan to extend these results to obtain general conditions for semiparametric inference after DNN estimation of nuisance components, which will allow for considerations such as more efficient estimation procedures, and instrumental variable settings.
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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 | Chad Brown (2024) Statistical Properties of Deep Neural Networks with Dependent Data | 1.000 | 22 | 3 | 100% |
| 2 | P. M. Robinson (1988) Root-N-Consistent Semiparametric Regression | 0.811 | 4 | 2 | 100% |
| 3 | Max H. Farrell, Tengyuan Liang, and Sanjog Misra (2021) Deep Neural Networks for Estimation and Inference | 0.737 | 3 | 2 | 100% |
| 4 | D. Bosq (1998) Nonparametric Statistics for Stochastic Processes. Lecture Notes in Statistics, Vol. 110 | 0.644 | 4 | 1 | 100% |
| 5 | Qizhao Chen, Vasilis Syrgkanis, and Morgane Austern (2022) Debiased Machine Learning without Sample-Splitting for Stable Estimators | 0.644 | 2 | 2 | 100% |
| 6 | Herold Dehling and Walter Philipp (2002) Empirical Process Techniques for Dependent Data | 0.644 | 2 | 2 | 100% |
| 7 | Peter L. Bartlett, Olivier Bousquet, and Shahar Mendelson (2005) Local Rademacher complexities | 0.585 | 3 | 1 | 100% |
| 8 | Xiaohong Chen (2007) Chapter 76 Large Sample Sieve Estimation of Semi-Nonparametric Models | 0.511 | 2 | 1 | 100% |
| 9 | Xiaohong Chen and Xiaotong Shen (1998) Sieve Extremum Estimates for Weakly Dependent Data | 0.511 | 2 | 1 | 100% |
| 10 | James Davidson (2022) Stochastic Limit Theory: An Introduction for Econometricians (second edition, second edition ed.) | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 34 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Statistical Properties of Deep Neural Networks with Dependent Data | 0.843 | 3 | 3 |
| 2 | Neighborhood Stability in Double/Debiased Machine Learning with Dependent Data | 0.737 | 3 | 2 |