Functional Coeï¬ƒcient Estimation with Both Categorical and Continuous Data
Liangjun Su and
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Ye Chen: Department of Economics, Princeton University
No 200909, Working Papers from University of California at Riverside, Department of Economics
We propose a local linear functional coeï¬ƒcient estimator that admits a mix of discrete and continuous 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 parametric and semi-parametric methods, our estimator performs better in both goodness-of-ï¬ t and in yielding economically interesting interpretation.
Keywords: Discrete variables; Functional coeï¬ƒcient estimation; Local linear estimation; Least squares cross validation. (search for similar items in EconPapers)
JEL-codes: C13 C14 (search for similar items in EconPapers)
Pages: 33 pages
Date: 2009-06, Revised 2009-06
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Persistent link: https://EconPapers.repec.org/RePEc:ucr:wpaper:200909
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