Estimating demand for differentiated products with error in market shares

Gandhi, Amit; Lu, Zhentong; Shi, Xun

doi:10.1920/wp.cem.2013.0313

Cited by 37 publications

(39 citation statements)

References 41 publications

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“…The test in (4.14) is not part of the tests discussed in Bugni, Canay, and Shi (2015) but has recently been used for a different testing problem in Gandhi, Lu, and Shi (2013). By construction,ĉ MR n (λ 1 − α) ≤ĉ PR n (λ 1 − α), and thus…”

Section: Power Advantage Over Subsampling Testsmentioning

confidence: 99%

Inference for subvectors and other functions of partially identified parameters in moment inequality models

Bugni

Canay²,

Shi

2017

Quantitative Economics

Self Cite

125

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This paper introduces a bootstrap-based inference method for functions of the parameter vector in a moment (in)equality model. These functions are restricted to be linear for two-sided testing problems, but may be nonlinear for one-sided testing problems. In the most common case, this function selects a subvector of the parameter, such as a single component. The new inference method we propose controls asymptotic size uniformly over a large class of data distributions and improves upon the two existing methods that deliver uniform size control for this type of problem: projection-based and subsampling inference. Relative to projection-based procedures, our method presents three advantages: (i) it weakly dominates in terms of finite sample power, (ii) it strictly dominates in terms of asymptotic power, and (iii) it is typically less computationally demanding. Relative to subsampling, our method presents two advantages: (i) it strictly dominates in terms of asymptotic power (for reasonable choices of subsample size), and (ii) it appears to be less sensitive to the choice of its tuning parameter than subsampling is to the choice of subsample size.

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Section: Power Advantage Over Subsampling Testsmentioning

confidence: 99%

Inference for subvectors and other functions of partially identified parameters in moment inequality models

Bugni

Canay²,

Shi

2017

Quantitative Economics

Self Cite

125

View full text Add to dashboard Cite

show abstract

“…is not part of the tests discussed in Bugni et al (2014) but has recently been used for a different testing problem in Gandhi et al (2013). By construction,ĉ…”

Section: Power Advantage Over Subsampling Testsmentioning

confidence: 99%

Inference for functions of partially identified parameters in moment inequality models

Bugni¹,

Canay²,

Shi³

2015

Self Cite

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractThis paper introduces a bootstrap-based inference method for functions of the parameter vector in a moment (in)equality model. As a special case, our method yields marginal confidence sets for individual coordinates of this parameter vector. Our inference method controls asymptotic size uniformly over a large class of data distributions. The current literature describes only two other procedures that deliver uniform size control for this type of problem: projection-based and subsampling inference. Relative to projection-based procedures, our method presents three advantages: (i) it weakly dominates in terms of finite sample power, (ii) it strictly dominates in terms of asymptotic power, and (iii) it is typically less computationally demanding. Relative to subsampling, our method presents two advantages: (i) it strictly dominates in terms of asymptotic power (for reasonable choices of subsample size), and (ii) it appears to be less sensitive to the choice of its tuning parameter than subsampling is to the choice of subsample size.

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“…The range of this mapping excludes some distributions of (s, X, Z); for instance, distributions in which some of the market shares take zero values with non-zero probability cannot be generated by our model, due to the multinomial logit structure. See Gandhi, Lu, and Shi (2013) for additional discussion of estimating discrete-choice demand models when some of the products are observed to have zero market shares.…”

Section: Identificationmentioning

confidence: 99%

Estimation of random coefficients logit demand models with interactive fixed effects

Moon¹,

Shum²,

Weidner³

2017

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We extend the Berry, Levinsohn and Pakes (BLP, 1995) random coefficients discretechoice 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 accommodates endogeneity and, at the same time, captures 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.

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Estimating demand for differentiated products with error in market shares

Cited by 37 publications

References 41 publications

Inference for subvectors and other functions of partially identified parameters in moment inequality models

Inference for subvectors and other functions of partially identified parameters in moment inequality models

Inference for functions of partially identified parameters in moment inequality models

Estimation of random coefficients logit demand models with interactive fixed effects

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