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Estimating High-Dimensional Discrete Choice Model of Differentiated Products with Random Coefficients

Masayuki Sawada, Kohei Kawaguchi

arXiv 19 Apr 2020 · Econometrics

arXiv:2004.08791 · PDF · DOI · OpenAlex · Extracted main text

Abstract

We propose an estimation procedure for discrete choice models of differentiated products with possibly high-dimensional product attributes. In our model, high-dimensional attributes can be determinants of both mean and variance of the indirect utility of a product. The key restriction in our model is that the high-dimensional attributes affect the variance of indirect utilities only through finitely many indices. In a framework of the random-coefficients logit model, we show a bound on the error rate of a $l_1$-regularized minimum distance estimator and prove the asymptotic linearity of the de-biased estimator.

Citation extraction

11
references
30
in-text mentions
11
distinct cited
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11,763
main-text words

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Most heavily cited references

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.

ReferenceIntensityMentionsSectionsMain text
1Belloni, A., V. Chernozhukov, D. Chetverikov, C. Hansen, and K. Kato (2018) High-Dimensional Econometrics and Regularized GMM1.000135100%
2Ledoux, M. and M. Talagrand (1991) Probability in Banach Spaces: Isoperimetry and Processes1.00075100%
3Berry, S., O. B. Linton, and A. Pakes (2004) Limit Theorems for Estimating the Parameters of Differentiated Product Demand Systems0.51121100%
4Armstrong, T. B (2016) Large Market Asymptotics for Differentiated Product Demand Estimators with Economic Models of Supply0.40511100%
5Berry, S., J. Levinsohn, and A. Pakes (1995) Automobile Prices in Market Equilibrium0.40511100%
6Berry, S. T (1994) Estimating Discrete-Choice Models of Product Differentiation0.40511100%
7Foster, D. J. and A. Rakhlin (2019) $l_infty$ Vector Contraction for Rademacher Complexity0.40511100%
8Frank, I. and J. Friedman (1993) A Statistical View of Some Chemometrics Regression Tools0.40511100%
9Gillen, B. J., S. Montero, H. R. Moon, and M. Shum (2019) BLP-2LASSO for Aggregate Discrete Choice Models with Rich Covariates0.40511100%
10Tibshirani, R (1996) Regression Shrinkage and Selection via the Lasso0.40511100%

Showing the top 10 of 11 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Estimation of BLP models with high-dimensional controls0.51121