A Simple Nonparametric Estimator for the Distribution of Random Coefficients

Bajari, Patrick; Fox, Jeremy T.; Kim, Kyoo il; Ryan, Stephen P.

doi:10.3386/w15210

Cited by 14 publications

(17 citation statements)

References 61 publications

(75 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our work complements some recent papers in which alternative estimation approaches and extensions of the standard random coefficients logit model have been proposed, including Villas-Boas and Winer (1999), Knittel and Metaxoglou (2014), Dube et al (2012), Harding and Hausman (2007), Bajari et al (2011), and Gandhi et al (2010).…”

Section: Introductionmentioning

confidence: 56%

“…17 As such, our identification results do not add to the literature on non-parametric identification of the BLP model (as in Berry and Haile (2014), Chiappori and Komunjer (2009), Bajari et al (2011)); our concern is, rather, to show that the logit demand model with parametrically-distributed random coefficients can still be identified after the introduction of the interactive fixed effects.…”

mentioning

confidence: 77%

“…Recent papers have addressed estimation when this is not known; e.g. Bajari et al (2011), Bajari et al (2012.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Estimation of Random Coefficients Logit Demand Models with Interactive Fixed Effects

2017

View full text Add to dashboard Cite

JEL classification: C23 C25 Keywords:Discrete-choice demand model Interactive fixed effects Factor analysis Panel data Random utility model a b s t r a c tWe 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.

show abstract

Section: Introductionmentioning

confidence: 56%

mentioning

confidence: 77%

See 1 more Smart Citation

Estimation of Random Coefficients Logit Demand Models with Interactive Fixed Effects

2017

View full text Add to dashboard Cite

show abstract

“…Our work complements some recent papers in which alternative estimation approaches and extensions of the standard random coefficients logit model have been proposed, including Villas-Boas and Winer (1999), Knittel and Metaxoglou (2008), Dube, Fox and Su (2012), Harding and Hausman (2007), Bajari, Fox, Kim and Ryan (2011), and Gandhi, Kim and Petrin (2010).…”

Section: Introductionmentioning

confidence: 56%

Estimation of random coefficients logit demand models with interactive fixed effects

Weidner¹,

Moon²,

Shum³

2012

View full text Add to dashboard Cite

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 application to US automobile demand.

show abstract

“…4 In this paper, we prove that the distribution F (β) is nonparametrically identified, in the sense that the true F 0 , α J,0 is the only the pair F, α J that solves Pr (j | x, w) ≡ Pr j | x; F, α J in (2) for all j and (x, w), where Pr (j | x, w) denotes the population choice probabilities. We first recover the homogeneous terms, α J (or the fixed parameters α and γ w ) in (2). We then provide two identification arguments, one of which is constructive and the other of which is non-constructive.…”

mentioning

confidence: 99%

The random coefficients logit model is identified

Fox

Kim

Ryan³

et al. 2012

Journal of Econometrics

Self Cite

View full text Add to dashboard Cite

The random coefficients multinomial choice logit model, also known as the mixed logit, has been widely used in empirical choice analysis for the last thirty years. We prove that the distribution of random coefficients in the multinomial logit model is nonparametrically identified. Our approach requires variation in product characteristics only locally and does not rely on the special regressors with large supports used in related papers. One of our two identification arguments is constructive. Both approaches may be applied to other choice models with random coefficients.

show abstract

A Simple Nonparametric Estimator for the Distribution of Random Coefficients

Cited by 14 publications

References 61 publications

Estimation of Random Coefficients Logit Demand Models with Interactive Fixed Effects

Estimation of Random Coefficients Logit Demand Models with Interactive Fixed Effects

Estimation of random coefficients logit demand models with interactive fixed effects

The random coefficients logit model is identified

Contact Info

Product

Resources

About