AI 中文总结
研究基于微观数据的半非参数差异化产品需求模型，开发轮廓筛最小距离估计器，利用消费者协变量变化恢复函数与截距，经排除价格工具分离截距，证明市场数量和样本量增长时估计器渐近可忽略且能一致估计，蒙特卡罗证据支持该结果。
AI 中文摘要
本文为具有微观层面选择数据的半非参数差异化产品需求模型开发了一种轮廓筛最小距离估计器。该估计器以Berry和Haile（2024）为基础，利用消费者协变量在市场内的变化来恢复灵活的消费者异质性函数和特定市场的复合截距。然后，排除的价格工具将这些截距分离为灵活的价格侧函数和结构需求冲击。主要统计挑战在于轮廓市场截距的数量随市场数量增长。研究表明，当市场数量和市场内最小样本量都增长时，此轮廓步骤渐近可忽略，且共同结构函数可一致估计。蒙特卡罗证据支持一致性结果，并说明了灵活价格侧估计在反事实需求分析中的价值。
英文摘要
This paper develops a profiled sieve minimum-distance estimator for a semi-nonparametric differentiated-products demand model with micro-level choice data. Building on Berry and Haile (2024), the estimator uses within-market variation in consumer covariates to recover a flexible consumer-heterogeneity function and market-specific composite intercepts. Excluded price instruments then separate these intercepts into a flexible price-side function and structural demand shocks. The main statistical challenge is that the number of profiled market intercepts grows with the number of markets. I show that, when both the number of markets and the minimum within-market sample size grow, this profiling step is asymptotically negligible and the common structural functions are uniformly consistently estimated. Monte Carlo evidence supports the consistency result and illustrates the value of flexible price-side estimation for counterfactual demand analysis.
Comments22 pages (63 with appendix), 4 figures, 2 tables. Revised exposition and assumptions; expanded proofs
