Amandeep Singh, Ye Liu, Hema Yoganarasimhan
arXiv 13 Jul 2023 · Econometrics · 2 citations (OpenAlex)
arXiv:2307.07090 · PDF · DOI · OpenAlex · Extracted main text
Choice modeling is at the core of understanding how changes to the competitive landscape affect consumer choices and reshape market equilibria. In this paper, we propose a fundamental characterization of choice functions that encompasses a wide variety of extant choice models. We demonstrate how non-parametric estimators like neural nets can easily approximate such functionals and overcome the curse of dimensionality that is inherent in the non-parametric estimation of choice functions. We demonstrate through extensive simulations that our proposed functionals can flexibly capture underlying consumer behavior in a completely data-driven fashion and outperform traditional parametric models. As demand settings often exhibit endogenous features, we extend our framework to incorporate estimation under endogenous features. Further, we also describe a formal inference procedure to construct valid confidence intervals on objects of interest like price elasticity. Finally, to assess the practical applicability of our estimator, we utilize a real-world dataset from S. Berry, Levinsohn, and Pakes (1995). Our empirical analysis confirms that the estimator generates realistic and comparable own- and cross-price elasticities that are consistent with the observations reported in the existing literature.
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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 | S. Berry, J. Levinsohn, and A. Pakes (1995) Automobile prices in market equilibrium | 1.000 | 13 | 6 | 100% |
| 2 | G. Compiani (2022) Market counterfactuals and the specification of multiproduct demand: A nonparametric approach | 0.956 | 8 | 4 | 88% |
| 3 | V. Chernozhukov, W. K. Newey, V. Quintas-Martinez, and V. Syrgkanis (2021) Automatic debiased machine learning via neural nets for generalized linear regression | 0.941 | 6 | 3 | 83% |
| 4 | M. Zaheer, S. Kottur, S. Ravanbakhsh, B. Poczos, R. R. Salakhutdinov… (2017) Deep sets | 0.843 | 3 | 3 | 100% |
| 5 | J. Blanchet, G. Gallego, and V. Goyal (2016) A markov chain approximation to choice modeling | 0.737 | 3 | 3 | 67% |
| 6 | M. S. Goeree (2008) Limited information and advertising in the us personal computer industry | 0.737 | 3 | 3 | 67% |
| 7 | J. A. Hausman and D. A. Wise (1978) A conditional probit model for qualitative choice: Discrete decisions recognizing interdependence and heterogeneous preferences | 0.737 | 3 | 3 | 67% |
| 8 | N. Mehta, S. Rajiv, and K. Srinivasan (2003) Price uncertainty and consumer search: A structural model of consideration set formation | 0.737 | 3 | 3 | 67% |
| 9 | V. Chernozhukov, W. K. Newey, and R. Singh (2022) Automatic debiased machine learning of causal and structural effects | 0.644 | 4 | 1 | 100% |
| 10 | V. Chernozhukov, J. C. Escanciano, H. Ichimura, W. K. Newey, and J.… Locally robust semiparametric estimation | 0.644 | 3 | 2 | 67% |
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