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Fast and simple inner-loop algorithms of static / dynamic BLP estimations

Takeshi Fukasawa

arXiv 6 Apr 2024 · Econometrics · 1 citations (OpenAlex)

arXiv:2404.04494 · PDF · DOI · OpenAlex · Extracted main text

Abstract

This study investigates computationally efficient inner-loop algorithms for estimating static/dynamic BLP models. It provides the following ideas for reducing the number of inner-loop iterations: (1). Add a term relating to the outside option share in the BLP contraction mapping; (2). Analytically represent the mean product utilities as a function of value functions and solve for value functions (for dynamic BLP); (3). Combine an acceleration method of fixed-point iterations, especially the Anderson acceleration. They are independent and easy to implement. This study shows the good performance of these methods using numerical experiments.

Citation extraction

41
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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
1Nevo, A (2001) Measuring market power in the ready-to-eat cereal industry1.00083100%
2Sun, Y. and Ishihara, M (2019) A computationally efficient fixed point approach to dynamic structural demand estimation0.9568488%
3Pál, L. and Sándor, Z (2023) Comparing procedures for estimating random coefficient logit demand models with a special focus on obtaining global optima0.9507586%
4Gowrisankaran, G. and Rysman, M (2012) Dynamics of consumer demand for new durable goods0.9507486%
5Berry, S., Levinsohn, J., and Pakes, A (1999) Voluntary export restraints on automobiles: Evaluating a trade policy0.92810480%
6Berry, S., Levinsohn, J., and Pakes, A (1995) Automobile prices in market equilibrium0.92314579%
7Conlon, C. and Gortmaker, J (2020) Best practices for differentiated products demand estimation with PyBLP0.90215773%
8Lee, J. and Seo, K (2015) A computationally fast estimator for random coefficients logit demand models using aggregate data0.8947671%
9Dubé, J.-P., Fox, J. T., and Su, C.-L (2012) Improving the numerical performance of static and dynamic aggregate discrete choice random coefficients demand estimation0.86011664%
10Berry, S. T (1994) Estimating discrete-choice models of product differentiation0.8434375%

Showing the top 10 of 41 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
1Sequential algorithm for structural estimations with equilibrium constraints0.860114