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SET IDENTIFIED LINEAR MODELS

机译:设置识别的线性模型

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We analyze the identification and estimation of parameters B satisfying the incomplete linear moment restrictions E(zT(xB — y)) = E(zTu(z)), where z is a set of instruments and u(z) an unknown bounded scalar function. We first provide empirically relevant examples of such a setup. Second, we show that these conditions set identify B where the identified set B is bounded and convex. We provide a sharp characterization of the identified set not only when the number of moment conditions is equal to thenumber of parameters of interest, but also in the case in which the number of conditions is strictly larger than the number of parameters. We derive a necessary and sufficient condition of the validity of supernumerary restrictions which generalizes thefamiliar Sargan condition. Third, we provide new results on the asymptotics of analog estimates constructed from the identification results. When B is a strictly convex set, we also construct a test of the null hypothesis, BQ e B, whose size is asymptotically correct and which relies on the minimization of the support function of the set B — {Bo}. Results of some Monte Carlo experiments are presented.
机译:我们分析参数B的识别和估计,参数B满足不完全线性矩约束E(zT(xB — y))= E(zTu(z)),其中z是一组工具,u(z)是未知的有界标量函数。我们首先提供这种设置的经验相关示例。第二,我们证明这些条件集标识B,其中标识的集B是有界的和凸的。我们不仅在矩条件的数量等于感兴趣的参数的数量时,而且在条件的数量严格大于参数的数量的情况下,对所标识的集合进行清晰的描述。我们推导了额外限制的有效性的充要条件,该条件概括了熟悉的萨甘条件。第三,我们提供了关于根据鉴定结果构造的模拟估计的渐近性的新结果。当B是一个严格凸集时,我们还构造了一个零假设BQ e B的检验,该假设的大小是渐近正确的,并且依赖于对B — {Bo}的支持函数的最小化。给出了一些蒙特卡洛实验的结果。

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