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Functional Coefficient Estimation with Both Categorical and Continuous Data

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Author Info

  • Ye Chen

    (Department of Economics, Princeton University)

  • Liangjun Su

    ()
    (School of Economics, Singapore Management University)

  • Aman Ullah

    ()
    (Department of Economics, University of California Riverside)

Abstract

We propose a local linear functional coefficient estimator that admits a mix of discrete and contin- uous data for stationary time series. Under weak conditions our estimator is asymptotically normally distributed. A small set of simulation studies is carried out to illustrate the ï¬nite sample performance of our estimator. As an application, we estimate a wage determination function that explicitly allows the return to education to depend on other variables. We ï¬nd evidence of the complex interacting patterns among the regressors in the wage equation, such as increasing returns to education when experience is very low, high return to education for workers with several years of experience, and diminishing returns to education when experience is high. Compared with the commonly used para- metric and semi-parametric methods, our estimator performs better in both goodness-of-ï¬t and in yielding economically interesting interpretation.

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File URL: http://economics.ucr.edu/repec/ucr/wpaper/09-09.pdf
File Function: First version, 2009
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Bibliographic Info

Paper provided by University of California at Riverside, Department of Economics in its series Working Papers with number 200909.

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Length: 33 pages
Date of creation: Jun 2009
Date of revision: Jun 2009
Handle: RePEc:ucr:wpaper:200909

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Related research

Keywords: Discrete variables; Functional coefficient estimation; Local linear estimation; Least squares cross validation.;

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