Dynamic Models with Robust Decision Makers: Identification and Estimation
稳健决策者动态模型的识别与估计AI 解读 ↓
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稳健决策者动态模型的识别与估计
- 研究问题
- 研究决策者担心基准模型误设并按最坏情形决策时，基准模型、偏好参数及最坏情形模型能否识别和一致估计。
- 方法与思路
- 作者利用薄尾函数空间上的单调凸算子不动点结果建立延续价值理论，并通过扰动分析研究识别和估计。
- 材料陈述的贡献
- 摘要称，基准模型与偏好参数联合欠识别；固定基准后给出识别条件，并刻画延续价值和最坏情形模型的一致估计条件。
- 阅读关注点
- 应核对固定基准模型的识别含义、无界状态和效用下的不动点条件，以及资产定价应用中概率扭曲的解释。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未列出具体条件，应用中的时变悲观扭曲也缺少数值细节。
原文摘要
This paper studies identification and estimation of a class of dynamic models in which the decision maker (DM) is uncertain about the data-generating process. The DM surrounds a benchmark model that he or she fears is misspecified by a set of models. Decisions are evaluated under a worst-case model delivering the lowest utility among all models in this set. The DM's benchmark model and preference parameters are jointly underidentified. With the benchmark model held fixed, primitive conditions are established for identification of the DM's worst-case model and preference parameters. The key step in the identification analysis is to establish existence and uniqueness of the DM's continuation value function allowing for unbounded statespace and unbounded utilities. To do so, fixed-point results are derived for monotone, convex operators that act on a Banach space of thin-tailed functions arising naturally from the structure of the continuation value recursion. The fixed-point results are quite general; applications to models with learning and Rust-type dynamic discrete choice models are also discussed. For estimation, a perturbation result is derived which provides a necessary and sufficient condition for consistent estimation of continuation values and the worst-case model. The result also allows convergence rates of estimators to be characterized. An empirical application studies an endowment economy where the DM's benchmark model may be interpreted as an aggregate of experts' forecasting models. The application reveals time-variation in the way the DM pessimistically distorts benchmark probabilities. Consequences for asset pricing are explored and connections are drawn with the literature on macroeconomic uncertainty.