Yanhao, Wei, Zhenling Jiang
arXiv 7 Feb 2025 · Econometrics · publishedMarketing Science (2024) · 16 citations (OpenAlex)
arXiv:2502.04945 · PDF · DOI · OpenAlex · Extracted main text
We study an alternative use of machine learning. We train neural nets to provide the parameter estimate of a given (structural) econometric model, for example, discrete choice or consumer search. Training examples consist of datasets generated by the econometric model under a range of parameter values. The neural net takes the moments of a dataset as input and tries to recognize the parameter value underlying that dataset. Besides the point estimate, the neural net can also output statistical accuracy. This neural net estimator (NNE) tends to limited-information Bayesian posterior as the number of training datasets increases. We apply NNE to a consumer search model. It gives more accurate estimates at lighter computational costs than the prevailing approach. NNE is also robust to redundant moment inputs. In general, NNE offers the most benefits in applications where other estimation approaches require very heavy simulation costs. We provide code at: https://nnehome.github.io.
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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 | Chen (2007) Large Sample Sieve Estimation of Semi-nonparametric Models | 0.737 | 4 | 3 | 50% |
| 2 | White (1990) Connectionist Nonparametric Regression: Multilayer Feedforward Networks Can Learn Arbitrary Mappings | 0.737 | 4 | 3 | 50% |
| 3 | Farrell, Liang and Misra (2021) Deep Neural Networks for Estimation and Inference | 0.737 | 3 | 3 | 67% |
| 4 | Ursu (2018) The Power of Rankings: Quantifying the Effect of Rankings on Online Consumer Search and Purchase Decisions | 0.693 | 9 | 1 | 100% |
| 5 | Chen and Liao (2015) Select the Valid and Relevant Moments: An Information-based LASSO for GMM with Many Moments | 0.644 | 2 | 2 | 100% |
| 6 | Newey (2007) Generalized Method of Moments | 0.644 | 2 | 2 | 100% |
| 7 | White (1989) Learning in Artificial Neural Networks: A Statistical Perspective | 0.511 | 2 | 2 | 50% |
| 8 | Geweke and Keane (2001) Computationally Intensive Methods for Integration in Econometrics | 0.511 | 2 | 1 | 100% |
| 9 | Gourieroux, Monfort and Renault (1993) Indirect Inference | 0.511 | 2 | 1 | 100% |
| 10 | Kim (2002) Limited Information Likelihood and Bayesian Analysis | 0.511 | 2 | 1 | 100% |
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