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

Harold D. Chiang, Kengo Kato, Yuya Sasaki and Takuya Ura

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Abstract: 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.

Date: 2021-02
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (2)

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