Estimating Continuous Treatment Effects in Panel Data using Machine Learning with a Climate Application

This paper introduces and proves asymptotic normality for a new semi-parametric estimator of continuous treatment effects in panel data. Specifically, we estimate the average derivative. Our estimator uses the panel structure of data to account for unobservable time-invariant heterogeneity and machi...

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Veröffentlicht in:	arXiv.org 2023-09
Hauptverfasser:	Klosin, Sylvia, Vilgalys, Max
Format:	Artikel
Sprache:	eng
Schlagworte:	Crop yield Elasticity Estimates Heterogeneity Impact analysis Impact damage Machine learning Normality
Online-Zugang:	Volltext
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