Functional Coeï¬ƒcient Estimation with Both Categorical and Continuous Data
AbstractWe propose a local linear functional coeï¬ƒcient 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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Bibliographic InfoPaper provided by University of California at Riverside, Department of Economics in its series Working Papers with number 200909.
Length: 33 pages
Date of creation: Jun 2009
Date of revision: Jun 2009
Discrete variables; Functional coeï¬ƒcient estimation; Local linear estimation; Least squares cross validation.;
Find related papers by JEL classification:
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
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