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Local Partitioned Regression

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  • Norbert Christopeit
  • Stefan G. N. Hoderlein

Abstract

In this paper, we introduce a kernel-based estimation principle for nonparametric models named local partitioned regression (LPR). This principle is a nonparametric generalization of the familiar partition regression in linear models. It has several key advantages: First, it generates estimators for a very large class of semi- and nonparametric models. A number of examples that are particularly relevant for economic applications will be discussed in this paper. This class contains the additive, partially linear, and varying coefficient models as well as several other models that have not been discussed in the literature. Second, LPR-based estimators achieve optimality criteria: They have optimal speed of convergence and are oracle-efficient. Moreover, they are simple in structure, widely applicable, and computationally inexpensive. A Monte Carlo simulation highlights these advantages. Copyright The Econometric Society 2006.

Suggested Citation

  • Norbert Christopeit & Stefan G. N. Hoderlein, 2006. "Local Partitioned Regression," Econometrica, Econometric Society, vol. 74(3), pages 787-817, May.
    • Handle: RePEc:ecm:emetrp:v:74:y:2006:i:3:p:787-817
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    File URL: http://hdl.handle.net/10.1111/j.1468-0262.2006.00683.x
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    Cited by:

    1. Norbert Christopeit & Michael Massmann, 2013. "Estimating Structural Parameters in Regression Models with Adaptive Learning," Tinbergen Institute Discussion Papers 13-111/III, Tinbergen Institute.
    2. Berthold R. Haag, 2008. "Non‐parametric Regression Tests Using Dimension Reduction Techniques," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(4), pages 719-738, December.
    3. Marco Costanigro & Ron C. Mittelhammer & Jill J. McCluskey, 2009. "Estimating class‐specific parametric models under class uncertainty: local polynomial regression clustering in an hedonic analysis of wine markets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(7), pages 1117-1135, November.
    4. Damian Kozbur, 2013. "Inference in additively separable models with a high-dimensional set of conditioning variables," ECON - Working Papers 284, Department of Economics - University of Zurich, revised Apr 2018.

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