
handle: 10419/130018
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.
panel data, ddc:330, nonparametric regression, Classi?cation of regression curves, k-means clustering, kernel estimation, nonparametric regression, panel data, kernel estimation, k-means clustering, Classification of regression curves
panel data, ddc:330, nonparametric regression, Classi?cation of regression curves, k-means clustering, kernel estimation, nonparametric regression, panel data, kernel estimation, k-means clustering, Classification of regression curves
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