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SPLINE-BACKFITTED KERNEL SMOOTHING OF ADDITIVE COEFFICIENT MODEL

  • Rong Liu (a1) and Lijian Yang (a2)
Abstract

Additive coefficient model (Xue and Yang, 2006a, 2006b) is a flexible regression and autoregression tool that circumvents the “curse of dimensionality.” We propose spline-backfitted kernel (SBK) and spline-backfitted local linear (SBLL) estimators for the component functions in the additive coefficient model that are both (i) computationally expedient so they are usable for analyzing high dimensional data, and (ii) theoretically reliable so inference can be made on the component functions with confidence. In addition, they are (iii) intuitively appealing and easy to use for practitioners. The SBLL procedure is applied to a varying coefficient extension of the Cobb-Douglas model for the U.S. GDP that allows nonneutral effects of the R&D on capital and labor as well as in total factor productivity (TFP).

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Corresponding author
*Address correspondence to Rong Liu, Department of Mathematics, University of Toledo, Toledo, OH 43606, USA; e-mail: rong.liu@utoledo.edu.
Lijian Yang, Department of Statistics and Probability, Michigan State University, East Lansing, MI 48824, USA; e-mail: yang@stt.msu.edu.
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Econometric Theory
  • ISSN: 0266-4666
  • EISSN: 1469-4360
  • URL: /core/journals/econometric-theory
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