 Functional Coefficient Estimation with Both Categorical and Continuous DataYe Chen,
Liangjun Su () and
Aman Ullah
No 200909, Working Papers from University of California at Riverside, Department of Economics Abstract: We propose a local linear functional coefficient 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 coefficient estimation; Local linear estimation; Least squares cross validation. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ucr:wpaper:200909 Access Statistics for this paper More papers in Working Papers from University of California at Riverside, Department of Economics Contact information at EDIRC. |
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