Random Logit Model: An Application to US Soft Drink Differentiated Demand Estimation

Benaissa Chidmi ()
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Benaissa Chidmi: Department of Agricultural and Applied Economics, Texas Tech University

Chapter Chapter 3 in Applied Econometric Analysis Using Cross Section and Panel Data, 2023, pp 61-91 from Springer

Abstract: Abstract This chapter attempts to provide an overview of estimating demand for differentiated products using the random coefficient multinomial logit model, also known as the BLP model. The chapter discusses reasons for the BLP’s superiority over traditional demand models, such as the AIDS models. It also describes recent development in the BLP-type literature, especially dealing with computational issues. In addition, the chapter covers the empirical process to estimate the iBLP model, starting with the numerical integration methods, simulation of observed and unobserved consumers’ characteristics, contraction mapping, endogeneity, and the generalized method of moments (GMM) estimation, and the existing software and packages to estimate the random coefficient logit model. Finally, the chapter offers an empirical illustration for estimating differentiated demand for soft drinks.

Keywords: BLP; Random coefficient logit; Differentiated demand; Soft drinks; Python pyblp (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:conchp:978-981-99-4902-1_3

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DOI: 10.1007/978-981-99-4902-1_3

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