Nonparametric Gini-Frisch bounds

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 heterogeneous 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.

Automated summary

This paper generalizes the Gini-Frisch bounds to accommodate nonparametric heterogeneous effects and provides suitable conditions for their application in nonparametric nonseparable equations.