Nonparametric Gini-Frisch bounds

Karim Chalak

doi:10.1016/j.jeconom.2023.105560

Nonparametric Gini-Frisch bounds

Karim Chalak

Economics

Research output: Contribution to journal › Article › peer-review

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 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.

Original language	English
Article number	105560
Journal	Journal of Econometrics
Volume	238
Issue number	1
Early online date	8 Nov 2023
DOIs	https://doi.org/10.1016/j.jeconom.2023.105560
Publication status	Published - 1 Jan 2024

Keywords

Attenuation bias
Gini-Frisch bounds
Measurement error
Nonparametric nonseparable equation
Partial identification

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title = "Nonparametric Gini-Frisch bounds",

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 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.",

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note = "Funding Information: I thank the participants in the 89th Annual Meeting of the Southern Economic Association, the 2019 Triangle Econometrics Conference (Duke University), and the 2020 DC-MD-VA Econometrics Workshop as well as the participants in the seminars at Iowa State University, Queen Mary University of London, University of Quebec at Montreal, University of Montreal, University of Connecticut, Ohio State University, Georgetown University, and University of Manchester. I acknowledge support from the Bankard Fund for Political Economy and IVADO. I thank Haruka Takayama Hasegawa and Firmin Ayivodji for research assistance. Any errors are the author{\textquoteright}s responsibility. Publisher Copyright: {\textcopyright} 2023 The Author(s)",

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AB - 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.

KW - Attenuation bias

KW - Gini-Frisch bounds

KW - Measurement error

KW - Nonparametric nonseparable equation

KW - Partial identification

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Nonparametric Gini-Frisch bounds

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Keywords

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