SEMIPARAMETRIC ESTIMATION OF SEPARABLE MODELS WITH POSSIBLY LIMITED DEPENDENT VARIABLES
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因变量可能受限的可分模型半参数估计
- 研究问题
- 如何半参数估计可分模型的不同组成部分，并处理多个指数及受限因变量，在降低维数负担的同时保持计算可行性？
- 方法与思路
- 作者以广义剖面似然为核心，在对条件误差分布施加一定假设后构造估计程序，并讨论渐近理论、实现、模拟与应用。
- 材料陈述的贡献
- 摘要称方法可处理多个指数和受限因变量，并利用可分结构缓解维数灾难；模拟与应用显示其可行性及良好的有限样本表现。
- 阅读关注点
- 应核对可分结构和各组成部分的识别、条件误差分布限制，以及多指数与受限因变量下的算法实现和渐近结论。
- 阅读边界
- 仅据所给出版社摘要，无法判断具体误差分布假设、识别归一化或计算成本；小样本高精度的表述缺少设计与数值支持。
原文摘要
In this paper we introduce a general method for estimating semiparametrically the different components in separable models. The family of separable models is quite popular in economic research because this structure offers clear interpretation, has straightforward economic consequences, and is often justified by theory. This family is also of statistical interest because it allows us to estimate high-dimensional complexity semiparametrically without running into the curse of dimensionality. We consider even the case when multiple indices appear in the objective function; thus we can estimate models that are typical in economic analysis, such as those that contain limited dependent variables. The idea of the new method is mainly based on a generalized profile likelihood approach. Although this requires some hypotheses on the conditional error distribution, it yields a quite general usable method with low computational costs but high accuracy even for small samples. We give estimation procedures and provide some asymptotic theory. Implementation is discussed; simulations and an application demonstrate its feasibility and good finite-sample behavior.This research was financially supported by Dirección General de Investigación del Ministerio de Ciencia y Tecnología under research grants BEC2001-1121 and BEC2001-1270; Dirección General de Enseñanza Superior del Ministerio de Educación y Ciencia under Subprograma de estancias de investigadores españoles en centros de investigación españoles y extranjeros, ref. PR2000-0096; and by the Danish Social Science Research fund. We also thank M. Delgado, O. Linton, two anonymous referees, and all participants of the working group STAPH on functional statistics in Toulouse, the activities of which are available on line at http://www.lsp.upstlse.fr/Fp/Ferraty/staph.html.