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结构动态离散选择模型的反事实分析AI 解读

Abstract Discrete choice data allow researchers to recover differences in utilities, but these differences may not suffice to identify policy-relevant counterfactuals of interest. In fact, in the case of dynamic discrete choice models, only a narrow set of counterfactuals are point-identified. In this paper, we explore how much one can learn about counterfactual outcomes of interest within this framework. We focus on the partial identification of counterfactuals, while…

结构动态离散选择模型的线性工具变量估计AI 解读

In structural dynamic discrete choice models, unobserved or mis-measured state variables may lead to biased parameter estimates and misleading inference. In this paper, we show that instrumental variables can address such measurement problems when they relate to state variables that evolve exogenously from the perspective of individual agents (i.e., market-level states). We define a class of linear instrumental variables estimators that rely on Euler equations expressed in…

动态离散选择模型中反事实的识别AI 解读

Dynamic discrete choice (DDC) models are not identified nonparametrically, but the non‐identification of models does not necessarily imply the nonidentification of counterfactuals. We derive novel results for the identification of counterfactuals in DDC models, such as non‐additive changes in payoffs or changes to agents' choice sets. In doing so, we propose a general framework that allows the investigation of the identification of a broad class of counterfactuals (covering…

从市场份额到消费者类型：差异化产品需求估计中的对偶性AI 解读

SUMMARY A widely applied method for differentiated product demand estimation, introduced by Berry, Levinsohn and Pakes in 1995, is founded on matching observed and theoretical market shares of products. In this paper, we allow for discrete consumer tastes and derive an equivalent matching occurring in the consumer type space. The equivalence between the two formulations expresses a duality between market shares and consumer types. In applications where a large number of…