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Identification And Estimation Of Local Average Derivatives In Non-separable Models Without Monotonicity

机译:没有单调性的不可分模型中局部平均导数的辨识与估计

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摘要

In many structural economic models there are no good arguments for additive separability of the error. Recently, this motivated intensive research on non-separable structures. For instance, in Hoderlein and Mammen (2007) a non-separable model in the single equation case was considered, and it was established that in the absence of the frequently employed monotonicity assumption local average structural derivatives (LASD) are still identified. In this paper, we introduce an estimator for the LASD. The estimator we propose is based on local polynomial fitting of conditional quantiles. We derive its large sample distribution through a Bahadur representation, and give some related results, e.g. about the asymptotic behaviour of the quantile process. Moreover, we generalize the concept of LASD to include endogeneity of regressors and discuss the case of a multivariate dependent variable. We also consider identification of structured non-separable models, including single index and additive models. We discuss specification testing, as well as testing for endogeneity and for the impact of unobserved heterogeneity. We also show that fixed censoring can easily be addressed in this framework. Finally, we apply some of the concepts to demand analysis using British Consumer Data.
机译:在许多结构经济学模型中,对于误差的可加可分性没有很好的论据。最近,这激发了对不可分离结构的深入研究。例如,在Hoderlein和Mammen(2007)中,考虑了在单方程情况下的不可分离模型,并且可以确定,在没有频繁使用的单调性假设的情况下,仍然可以识别出局部平均结构导数(LASD)。在本文中,我们介绍了LASD的估算器。我们提出的估计量基于条件分位数的局部多项式拟合。我们通过Bahadur表示得出大量样本分布,并给出一些相关结果,例如关于分位数过程的渐近行为。此外,我们概括了LASD的概念以包括回归变量的内生性,并讨论了多元因变量的情况。我们还考虑识别结构化的不可分离模型,包括单指数模型和加性模型。我们讨论规范测试,以及内生性和未观察到的异质性影响的测试。我们还表明,在此框架中可以轻松解决固定审查问题。最后,我们将一些概念应用于使用英国消费者数据的需求分析。

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