In this article, I describe the algorithm proposed by Berry, Levinsohn, and Pakes (1995, Econometrica 63: 841–890) to fit the random-parameters logit demand model from product market shares. I present a new command, blp, for this estimator.
BerryS., LevinsohnJ., and PakesA.1995. Automobile prices in market equilibrium. Econometrica63: 841–890.
3.
BerryS., LevinsohnJ., and PakesA.1999. Voluntary export restraints on automobiles: Evaluating a trade policy. American Economic Review89: 400–430.
4.
ChamberlainG.1987. Asymptotic efficiency in estimation with conditional moment restrictions. Journal of Econometrics34: 305–334.
5.
DeatonA. S., and MuellbauerJ.1980a. Economics and Consumer Behavior.Cambridge: Cambridge University Press.
6.
DeatonA. S., and MuellbauerJ.1980b. An almost ideal demand system. American Economic Review70: 312–326.
7.
DrukkerD. M., and GatesR.2006. Generating Halton sequences using Mata. Stata Journal6: 214–228.
8.
DubéJ.-P., FoxJ. T., and SuC.-L.2012. Improving the numerical performance of static and dynamic aggregate discrete choice random coefficients demand estimation. Econometrica80: 2231–2267.
9.
HallA. R.2005. Generalized Method of Moments.Oxford: Oxford University Press.
10.
HoleA. R.2007. Fitting mixed logit models by using maximum simulated likelihood. Stata Journal7: 388–401.
11.
KnittelC. R., and MetaxoglouK.2014. Estimation of random-coefficient demand models: Two empiricists’ perspective. Review of Economics and Statistics96: 34–59.
12.
McFaddenD.1974. Conditional logit analysis of qualitative choice behavior. In Frontiers in Econometrics, ed. ZerembkaP., 105–142. New York: Academic Press.
NevoA.2000b. A practitioner's guide to estimation of random-coefficients logit models of demand. Journal of Economics and Management Strategy9: 513–548.
15.
NevoA.2001. Measuring market power in the ready-to-eat cereal industry. Econometrica69: 307–342.
16.
ReynaertM., and VerbovenF.2014. Improving the performance of random coefficients demand models: The role of optimal instruments. Journal of Econometrics179: 83–98.
17.
SkrainkaB. S., and JuddK. L.2011. High performance quadrature rules: How numerical integration affects a popular model of product differentiation. Working Papers CWP03/11, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
18.
TheilH.1965. The information approach to demand analysis. Econometrica33: 67–87.