arXiv 1 Dec 2021 · Econometrics
arXiv:2112.01377 · PDF · DOI · OpenAlex · Extracted main text
This paper explores the use of deep neural networks for semiparametric estimation of economic models of maximizing behavior in production or discrete choice. We argue that certain deep networks are particularly well suited as a nonparametric sieve to approximate regression functions that result from nonlinear latent variable models of continuous or discrete optimization. Multi-stage models of this type will typically generate rich interaction effects between regressors ("inputs") in the regression function so that there may be no plausible separability restrictions on the "reduced-form" mapping form inputs to outputs to alleviate the curse of dimensionality. Rather, economic shape, sparsity, or separability restrictions either at a global level or intermediate stages are usually stated in terms of the latent variable model. We show that restrictions of this kind are imposed in a more straightforward manner if a sufficiently flexible version of the latent variable model is in fact used to approximate the unknown regression function.
appendix boundary found by appendix_command · 66% 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 | Horowitz and Mammen (2007) Rate-Optimal Estimation for a General Class of Nonparametric Regression Models with Unknown Link Functions | 1.000 | 5 | 3 | 100% |
| 2 | Bauer and Kohler (2019) On Deep Learning as a Remedy for the Curse of Dimensionality in Nonparametric Regression | 0.928 | 4 | 3 | 100% |
| 3 | Farrell, Liang, and Misra (2019) Deep Neural Networks for Estimation and Inference | 0.855 | 8 | 4 | 62% |
| 4 | Mhaskar and Poggio (2016) Deep vs. Shallow Networks: an Approximation Theory Perspective | 0.843 | 3 | 3 | 100% |
| 5 | Cunha, Heckman, and Schennach (2010) Estimating the Technology of Cognitive and Noncognitive Skill Formation | 0.811 | 4 | 2 | 100% |
| 6 | Vovsha (1997) Application of Cross-Nested Logit Model to Mode Choice in Tel Aviv, Israel, Metropolitan Area | 0.811 | 4 | 2 | 100% |
| 7 | McFadden (1978) Modeling the Choice of Residential Location | 0.794 | 8 | 3 | 50% |
| 8 | Karpinski and Macintyre (1997) Polynomial Bounds for VC Dimension of Sigmoidal and General Pfaffian Neural Networks | 0.737 | 5 | 2 | 60% |
| 9 | Yarotsky (2017) Error Bounds for Approximations with Deep ReLU Networks | 0.737 | 3 | 3 | 67% |
| 10 | Eldan and Shamir (2016) The Power of Depth for Feedforward Networks | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 51 scored citations.