Estimation of Varying Coefficient Models with Measurement Error

Hao Dong (), Taisuke Otsu () and Luke Taylor
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Taisuke Otsu: London School of Economics and Political Science

No 1905, Departmental Working Papers from Southern Methodist University, Department of Economics

Abstract: We propose a semi-parametric estimator for varying coefficient models when the regressors in the nonparametric component are measured with error. Varying coefficient models are an extension of other popular semiparametric models, including partially linear and nonparametric additive models, and deliver an attractive solution to the curse-of-dimensionality. We use deconvolution kernel estimation in a two-step procedure and show that the estimator is consistent and asymptotically normally distributed. We do not assume that we know the distribution of the measurement error a priori, nor do we assume that the error is symmetrically distributed. Instead, we suppose we have access to a repeated measurement of the noisy regressor and use the approach of Li and Vuong (1998) based on Kotlarski�s (1967) identity. We show that the convergence rate of the estimator is significantly reduced when the distribution of the measurement error is assumed unknown and possibly asymmetric. Finally, we study the small sample behavior of our estimator in a simulation study.

Keywords: Varying coefficient models; deconvolution; classical measurement error; unknown error distribution. (search for similar items in EconPapers)
JEL-codes: C14 (search for similar items in EconPapers)
Date: 2019-09
New Economics Papers: this item is included in nep-ecm and nep-ore
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Citations: View citations in EconPapers (3)

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Related works:
Journal Article: Estimation of varying coefficient models with measurement error (2022)
Working Paper: Estimation of varying coefficient models with measurement error (2022)
Working Paper: Estimation of Varying Coefficient Models with Measurement Error (2019)
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