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Oracally Efficient Two-Step Estimation of Generalized Additive Model

Author

Listed:
  • Rong Liu
  • Lijian Yang
  • Wolfgang Karl Härdle

Abstract

Generalized additive models (GAM) are multivariate nonparametric regressions for non-Gaussian responses including binary and count data. We propose a spline-backfitted kernel (SBK) estimator for the component functions. Our results are for weakly dependent data and we prove oracle efficiency. The SBK techniques is both computational expedient and theoretically reliable, thus usable for analyzing high-dimensional time series. Inference can be made on component functions based on asymptotic normality. Simulation evidence strongly corroborates with the asymptotic theory.

Suggested Citation

  • Rong Liu & Lijian Yang & Wolfgang Karl Härdle, 2011. "Oracally Efficient Two-Step Estimation of Generalized Additive Model," SFB 649 Discussion Papers SFB649DP2011-016, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    • Handle: RePEc:hum:wpaper:sfb649dp2011-016
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    Keywords

    Bandwidths; B spline; knots; link function; mixing; Nadaraya-Watson estimator;
    All these keywords.

    JEL classification:

    • C00 - Mathematical and Quantitative Methods - - General - - - General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • J01 - Labor and Demographic Economics - - General - - - Labor Economics: General
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials

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