Nonparametric Gini-Frisch Bounds

Karim Chalak
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Abstract

The Gini-Frisch bounds partially identify the constant slope coefficient in a linear equation when the explanatory variable suffers from classical measurement error. This paper generalizes these quintessential bounds to accommodate nonparametric heterogenous effects. It provides suitable conditions under which the main insights that underlie the Gini-Frisch bounds apply to partially identify the average marginal effect of an error-laden variable in a nonparametric nonseparable equation. To this end, the paper puts forward a nonparametric analogue to the standard "forward" and "reverse" linear regression bounds. The nonparametric forward regression bound generalizes the linear regression "attenuation bias" due to classical measurement error.
非参数Gini-Frisch界
当解释变量存在经典测量误差时,Gini-Frisch边界可以部分识别线性方程中的常斜率系数。本文推广了这些典型的边界,以适应非参数异质效应。它提供了合适的条件,在这些条件下,Gini-Frisch边界的主要见解适用于部分地确定非参数不可分离方程中含误差变量的平均边际效应。为此,本文提出了标准的“正向”和“反向”线性回归边界的非参数模拟。非参数正回归界推广了由经典测量误差引起的线性回归“衰减偏差”。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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