Estimation of random coefficients logit demand models with interactive fixed effects
By H. R. Moon, M. Shum, Martin P. Weidner. 2012
41 citations recorded by the source corpus.
We extend the Berry, Levinsohn and Pakes (BLP, 1995) random coefficients discrete-choice demand model, which underlies much recent empirical work in IO. We add interactive fixed effects in the form of a factor structure on the unobserved product characteristics. The interactive fixed effects can be arbitrarily correlated with the observed product characteristics (including price), which accommodate endogeneity and, at the same time, capture strong persistence in market shares across products and markets. We propose a two-step least squares-minimum distance (LS-MD) procedure to calculate the estimator. Our estimator is easy to compute, and Monte Carlo simulations show that it performs well. We consider an empirical illustration to US automobile demand.
Subfields: Economics and Econometrics, Marketing
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In Economics and Econometrics
- Francisco J. R. Ruiz
- Rob Donnelly
- David M. Blei
- Susan Athey
- David McAdams
- Philip J. Reny
- Jakub Kastl
- Arnaud Costinot
In Marketing
- Susan Athey
- Francisco J. R. Ruiz
- David M. Blei
- Rob Donnelly
- Guido W. Imbens
- Arnaud Costinot
- Angelo Mele
Citations in the corpus
- Counterfactual inference for consumer choice across many product categories (2019, methodology): “7This approach has similarities to the econometrics literature on “interactive fixed effects models” although that literature has focused primarily on decomposing common trends across individuals over time rather than identifying common preferences for products across individuals (Moon and Weidner, ”
- Estimation and Inference about Heterogeneous Treatment Effects in High-Dimensional Dynamic Panels. (2017, unknown): “…Bonhomme and Manresa (2015), Moon and Weidner (2015), Moon and Weidner (2016), Kock (2016b), Manresa (2016), Lu and Su (2016), Su et al. (2016), Moon et al. (2018), Kock and Tang (2019), Bonhomme et al. (2019a), Bonhomme et al. (2019b), Gao and Li (2019), Chen et al. (2020), Lu and Su (2020))…”