论文标题
复杂结构模型的近似最大似然
Approximate Maximum Likelihood for Complex Structural Models
论文作者
论文摘要
间接推理（I-I）是一种流行的技术，用于估算复杂参数模型的可能性功能是棘手的，但是，I-I估计的统计效率值得怀疑。虽然Gallant和Tauchen（1996）的有效时刻方法承诺效率，但要为此效率付出的代价是降低效率的损失，从而潜在地缺乏模型错误指定的鲁棒性。这与更简单的I-I估计策略相反，I-I估计策略众所周知，由于它们对基础结构模型的特定元素的关注，因此对模型错误指定的敏感性较小。在这项研究中，我们提出了一种新的基于模拟的方法，该方法保持I-I估计的简约，这通常在经验应用中至关重要，但也可以提供与最大可能性一样有效的估计器。这种新方法是基于对结构模型的约束近似，该模型可确保识别并可以提供几乎有效的估计器。我们通过几个示例证明了这种方法，并表明这种方法可以提供估计量几乎与最大可能性一样有效，但可以在可行的情况下，但可以在最大可能性不可行的许多情况下使用。
Indirect Inference (I-I) is a popular technique for estimating complex parametric models whose likelihood function is intractable, however, the statistical efficiency of I-I estimation is questionable. While the efficient method of moments, Gallant and Tauchen (1996), promises efficiency, the price to pay for this efficiency is a loss of parsimony and thereby a potential lack of robustness to model misspecification. This stands in contrast to simpler I-I estimation strategies, which are known to display less sensitivity to model misspecification precisely due to their focus on specific elements of the underlying structural model. In this research, we propose a new simulation-based approach that maintains the parsimony of I-I estimation, which is often critical in empirical applications, but can also deliver estimators that are nearly as efficient as maximum likelihood. This new approach is based on using a constrained approximation to the structural model, which ensures identification and can deliver estimators that are nearly efficient. We demonstrate this approach through several examples, and show that this approach can deliver estimators that are nearly as efficient as maximum likelihood, when feasible, but can be employed in many situations where maximum likelihood is infeasible.
