SIMPLE TWO-STAGE INFERENCE FOR A CLASS OF PARTIALLY IDENTIFIED MODELS
提出一种新的两阶段估计和推断方法，适用于部分识别模型，无需调节参数、非保守且计算简单，可应用于静态进入博弈、投票博弈和离散混合模型。
This paper proposes a new two-stage estimation and inference procedure for a class of partially identified models. The procedure can be considered an extension of classical minimum distance estimation procedures to accommodate inequality constraints and partial identification. It involves no tuning parameter, is nonconservative, and is conceptually and computationally simple. The class of models includes models of interest to applied researchers, including the static entry game, a voting game with communication, and a discrete mixture model. Besides, a technical contribution is an implicit correspondence lemma which generalizes the implicit function theorem to multivalued implicit maps.