Linear programming approach to nonparametric inference under shape restrictions: with an application to regression kink designs

Fiche du document

Date

12 février 2021

Type de document
Périmètre
Identifiant
  • 2102.06586
Collection

arXiv

Organisation

Cornell University



Sujets proches En

Shape Forms (Shapes)

Citer ce document

Harold D. Chiang et al., « Linear programming approach to nonparametric inference under shape restrictions: with an application to regression kink designs », arXiv - économie


Partage / Export

Résumé 0

We develop a novel method of constructing confidence bands for nonparametric regression functions under shape constraints. This method can be implemented via a linear programming, and it is thus computationally appealing. We illustrate a usage of our proposed method with an application to the regression kink design (RKD). Econometric analyses based on the RKD often suffer from wide confidence intervals due to slow convergence rates of nonparametric derivative estimators. We demonstrate that economic models and structures motivate shape restrictions, which in turn contribute to shrinking the confidence interval for an analysis of the causal effects of unemployment insurance benefits on unemployment durations.

document thumbnail

Par les mêmes auteurs

Sur les mêmes sujets

Sur les mêmes disciplines

Exporter en