Differentiated Products Demand Systems from a Combination of Micro and Macro Data: The New Car Market
利用四种数据来源（产品特征、家庭属性、家庭第一与第二辆车选择匹配、家庭属性与首选车匹配）来估计新车市场的产品级需求系统，揭示复杂替代模式，并指出各数据源对解决传统估计问题的贡献。
In this paper, we exploit new sources of cross-sectional data to estimate a detailed product-level demand system for new passenger vehicles. We use four data sources: on the characteristics of products, on the attributes of the U.S. population of households, on the match between the first and second vehicle choices of the household, and on the match between households attributes and first choice vehicles. We show that these data solve some, but not all, of the traditional problems in estimating differentiated products demand systems and indicate which data sources are important for which problem. The data is rich enough to reveal a rather complex substitution pattern, requiring a quite general modeling framework. Together the data and model make a detailed analysis of industry demand possible. 1 Introduction In Berry, Levinsohn, and Pakes (1995) (BLP) we provide an algorithm for obtaining estimates of demand parameters for a class of differentiated product models. Demand-side models ...