Error-in-Variables Jump Regression Using Local Clustering

Yicheng Kang, Xiaodong Gong (), Jiti Gao and Peihua Qiu ()

No 13/16, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: Error-in-variables regression is widely used in econometric models. The statistical analysis becomes challenging when the regression function is discontinuous and the distribution of measurement error is unknown. In this paper, we propose a novel jump-preserving curve estimation method. A major feature of our method is that it can remove the noise effectively while preserving the jumps well, without requiring much prior knowledge about the measurement error distribution. The jump-preserving property is achieved mainly by local clustering. We show that the proposed curve estimator is statistical consistent, and it performs favourably, in comparison with an existing jump-preserving estimator. Finally, we demonstrate our method by an application to a health tax policy study in Australia.

Keywords: clustering; demand for private health insurance; kernel smoothing; local regression; measurement errors; price elasticity (search for similar items in EconPapers)
JEL-codes: C13 C14 (search for similar items in EconPapers)
Pages: 49
Date: 2016
New Economics Papers: this item is included in nep-ecm
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