Classification of nonparametric regression functions in heterogeneous panels

Michael Vogt and Oliver Linton
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Michael Vogt: Institute for Fiscal Studies

No CWP06/15, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

Abstract: We investigate a nonparametric panel model with heterogeneous regression functions. In a variety of applications, it is natural to impose a group structure on the regression curves. Specifically, we may suppose that the observed individuals can be grouped into a number of classes whose members all share the same regression function. We develop a statistical procedure to estimate the unknown group structure from the observed data. Moreover, we derive the asymptotic properties of the procedure and investigate its finite sample performance by means of a simulation study and a real-data example.

Keywords: Classi?cation of regression curves; k-means clustering; kernel estimation; nonparametric regression; panel data (search for similar items in EconPapers)
Date: 2015-02-20
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (6)

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Working Paper: Classification of nonparametric regression functions in heterogeneous panels (2015)
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