Greg Lewis, Bora Ozaltun, Georgios Zervas
arXiv 24 Nov 2021 · Econometrics
arXiv:2111.12397 · PDF · DOI · OpenAlex · Extracted main text
We discuss estimation of the differentiated products demand system of Berry et al (1995) (BLP) by maximum likelihood estimation (MLE). We derive the maximum likelihood estimator in the case where prices are endogenously generated by firms that set prices in Bertrand-Nash equilibrium. In Monte Carlo simulations the MLE estimator outperforms the best-practice GMM estimator on both bias and mean squared error when the model is correctly specified. This remains true under some forms of misspecification. In our simulations, the coverage of the ML estimator is close to its nominal level, whereas the GMM estimator tends to under-cover. We conclude the paper by estimating BLP on the car data used in the original Berry et al (1995) paper, obtaining similar estimates with considerably tighter standard errors.
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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 | 7 | 5 | 100% |
| 2 | C. Conlon and J. Gortmaker (2020) Best practices for differentiated products demand estimation with pyblp | 0.956 | 8 | 4 | 88% |
| 3 | A. Gandhi and J.-F. Houde (2019) Measuring substitution patterns in differentiated-products industries | 0.843 | 3 | 3 | 100% |
| 4 | G. Chamberlain (1987) Asymptotic efficiency in estimation with conditional moment restrictions | 0.737 | 3 | 2 | 100% |
| 5 | I. Andrews, M. Gentzkow, and J. M. Shapiro (2017) Measuring the sensitivity of parameter estimates to estimation moments | 0.644 | 2 | 2 | 100% |
| 6 | M. Reynaert and F. Verboven (2014) Improving the performance of random coefficients demand models: the role of optimal instruments | 0.644 | 2 | 2 | 100% |
| 7 | J.-P. Dubé, J. T. Fox, and C.-L. Su (2012) Improving the numerical performance of static and dynamic aggregate discrete choice random coefficients demand estimation | 0.511 | 2 | 1 | 100% |
| 8 | J. Abaluck and A. Adams-Prassl (2021) What do consumers consider before they choose? identification from asymmetric demand responses | 0.405 | 1 | 1 | 100% |
| 9 | T. B. Armstrong (2016) Large market asymptotics for differentiated product demand estimators with economic models of supply | 0.405 | 1 | 1 | 100% |
| 10 | S. T. Berry (1994) Estimating discrete-choice models of product differentiation | 0.405 | 1 | 1 | 100% |
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