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Nonparametric
Regression under Alternative Data Environments Abdoul G. Sam and Alan P. Ker |
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Abstract | |
This
paper proposes a nonparametric regression estimator which can accommodate
two empirically relevant data environments. The first data environment
assumes that at least one of the explanatory variables is discrete. In
such an environment, a “cell” approach which consists of partitioning
the data and estimating a separate regression for each cell has usually
been employed. The second data environment assumes that one needs to estimate
a set of regression functions that belong to different experimental units.
In both environments the proposed estimator attempts to reduce estimation
error by incorporating extraneous data from the other experimental units
or cells when estimating the regression function for a given individual
experimental unit or cell. Consistency and asymptotic normality of the
proposed estimator are established. Its computational simplicity and simulation
results demonstrate a strong potential in empirical applications. |
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© 2007 Dept. of Agricultural & Resource Economics, The University of Arizona
Send comments or questions to arecweb@ag.arizona.edu
Last updated September 15, 2004
Document located at http://ag.arizona.edu/arec/pubs/researchpapers/abstract2004-02.html