Conditional choice probability estimation with an imperfectly measured latent state
中文摘要
传统的动态离散选择模型——条件选择概率估计量（DDCM-CCP）文献仅考虑了利用纵向选择的似然进行识别。然而，由于选择变量是离散的且通常维度较小，这种方法在识别潜在状态的灵活动态方面存在局限。本文扩展了这一文献，利用对潜在离散状态的不完全测量，即所谓的代理变量。我首先证明代理变量能够改进识别，并讨论针对代理变量的调查设计如何影响识别条件。随后，我将 Arcidiacono 和 Miller (2011) 的估计量加以扩展，以汇集来自含噪代理变量非平衡面板的信息，从而能够估计比马尔可夫链更灵活的潜在状态动态。作为应用，我估计了一个劳动供给与心理健康的动态模型，其中动态的心理健康——只能被不完全观测——发挥着核心作用。结果揭示了比标准马尔可夫链更为复杂的动态。
Abstract
Traditional dynamic discrete choice model—Conditional Choice Probability estimator (DDCM‐CCP) literature considered identification using the likelihood of longitudinal choice only. However, such an approach has a limitation in identifying flexible dynamics for latent state due to the discrete and typically small‐dimensional nature of choice. This paper extends this literature to utilize imperfect measurements, called proxies, for a latent discrete state. I first show that proxies improve identification and discuss how survey design on proxies affects identification conditions. I then extend the estimator from Arcidiacono and Miller (2011) to pool information from an unbalanced panel of noisy proxies, enabling estimation of more flexible latent state dynamics than Markov chains. As an application, I estimate a dynamic model of labor supply and mental health, where dynamic mental health only imperfectly observed—plays a central role. The results reveal more complex dynamics than a standard Markov chain.