This paper studies computationally and theoretically attractive estimators called the Laplace type estimators (LTE), which include means and quantiles of Quasi-posterior distributions defined as transformations of general (non-likelihood-based) statistical criterion functions, such as those in GMM, nonlinear IV, empirical likelihood, and minimum distance methods. The approach generates an alternative to classical extremum estimation and also falls outside the parametric Bayesian approach. For example, it offers a new attractive estimation method for such important semi-parametric problems as censored and instrumental quantile, nonlinear GMM and value-at-risk models. The LTE's are computed using Markov Chain Monte Carlo methods, which help circumvent the computational curse of dimensionality. A large sample theory is obtained for regular cases.
翻译:本文在计算和理论上具有吸引力的估测器称为Laplace类型估计器(LTE),其中包括作为一般(非类似)统计标准功能(如GMM、非线性四、经验可能性和最低距离方法)的转换而定义的准差量分布手段和量数。这一方法为传统外表估计提供了一种替代方法,也不属于对等贝叶学方法的范围。例如,它为受检查和工具定量、非线性GM和风险价值模型等重要的半参数问题提供了一种新的有吸引力的估计方法。LTE是使用Markov链式蒙特卡洛方法计算的,这些方法有助于绕过对维度的计算诅咒。对于经常案例,可以获得大量的抽样理论。