arXiv 8 Jun 2021 · Econometrics
arXiv:2106.04636 · PDF · DOI · OpenAlex · Extracted main text
We show how the random coefficient logistic demand (BLP) model can be phrased as an automatically differentiable moment function, including the incorporation of numerical safeguards proposed in the literature. This allows gradient-based frequentist and quasi-Bayesian estimation using the Continuously Updating Estimator (CUE). Drawing from the machine learning literature, we outline hitherto under-utilized best practices in both frequentist and Bayesian estimation techniques. Our Monte Carlo experiments compare the performance of CUE, 2S-GMM, and LTE estimation. Preliminary findings indicate that the CUE estimated using LTE and frequentist optimization has a lower bias but higher MAE compared to the traditional 2-Stage GMM (2S-GMM) approach. We also find that using credible intervals from MCMC sampling for the non-linear parameters together with frequentist analytical standard errors for the concentrated out linear parameters provides empirical coverage closest to the nominal level. The accompanying admest Python package provides a platform for replication and extensibility.
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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 | Hong, Han, Li, Huiyu, Li, Jessie (2020) BLP estimation using Laplace transformation and overlapping simulation draws | 1.000 | 10 | 5 | 100% |
| 2 | Berry, Steven, Levinsohn, James, Pakes, Ariel (1995) Automobile prices in market equilibrium | 1.000 | 8 | 3 | 100% |
| 3 | Forneron, Jean-Jacques, Ng, Serena (2020) Inference by Stochastic Optimization: A Free-Lunch Bootstrap | 1.000 | 6 | 3 | 100% |
| 4 | Conlon, Christopher, Gortmaker, Jeff (2020) Best practices for differentiated products demand estimation with pyblp | 0.964 | 19 | 7 | 89% |
| 5 | Newey, Whitney K, Windmeijer, Frank (2009) Generalized method of moments with many weak moment conditions | 0.928 | 4 | 4 | 100% |
| 6 | Chernozhukov, Victor, Hong, Han (2003) An MCMC approach to classical estimation | 0.928 | 4 | 3 | 100% |
| 7 | Hansen, Lars Peter, Heaton, John, Yaron, Amir (1996) Finite-sample properties of some alternative GMM estimators | 0.928 | 4 | 3 | 100% |
| 8 | Knittel, Christopher R, Metaxoglou, Konstantinos (2014) Estimation of random-coefficient demand models: two empiricists' perspective | 0.928 | 4 | 3 | 100% |
| 9 | Newey, Whitney K, Smith, Richard J (2004) Higher order properties of GMM and generalized empirical likelihood estimators | 0.928 | 4 | 3 | 100% |
| 10 | Nevo, Aviv (2000) A practitioner's guide to estimation of random-coefficients logit models of demand | 0.874 | 6 | 4 | 67% |
Showing the top 10 of 43 scored citations.