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cwp061515.pdf
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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.
Authors
![Person graphic](/sites/default/files/styles/square_desktop/public/2022-06/IFS-person-graphic.png?itok=hWCtTSrz)
Oliver Linton
![Person graphic](/sites/default/files/styles/square_desktop/public/2022-06/IFS-person-graphic.png?itok=hWCtTSrz)
Michael Vogt
Working Paper details
- DOI
- 10.1920/wp.cem.2015.0615
- Publisher
- IFS
Suggested citation
Linton, O and Vogt, M. (2015). Classification of nonparametric regression functions in heterogeneous panels. London: IFS. Available at: https://ifs.org.uk/publications/classification-nonparametric-regression-functions-heterogeneous-panels (accessed: 1 July 2024).
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