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Nonparametric Lag Selection for Additive Models based on the Smooth Backfitting Estimator

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DOI: 10.4236/tel.2011.12004    3,921 Downloads   8,376 Views  

ABSTRACT

This paper proposes a nonparametric FPE-like procedure based on the smooth backfitting estimator when the additive structure is a priori known. This procedure can be expected to perform well because of its well-known finite sample performance of the smooth backfitting estimator. Consistency of our procedure is established under very general conditions, including heteroskedasticity.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Z. Guo, L. Cao and Y. He, "Nonparametric Lag Selection for Additive Models based on the Smooth Backfitting Estimator," Theoretical Economics Letters, Vol. 1 No. 2, 2011, pp. 15-17. doi: 10.4236/tel.2011.12004.

References

[1] D. Tj?stheim and B. Auestad, “Nonparametric Identification of Nonlinear Time Series: Selecting Significant Lags,” Journal of the American Statistical Association, Vol. 89, No.428, 1994, pp. 1410-1419. doi:10.2307/2291003
[2] R. Tschernig and L. Yang, “Nonparametric Lag Selection for Time Series,” Journal of Time Series Analysis, Vol. 21, No. 4, 2000, pp. 457-585. doi:10.1111/1467-9892.00193
[3] B. Cheng and H. Tong, “On Consistent Nonparametric Order Determination and Chaos,” Journal of the Royal Statistical Society series B (Methodological), Vol. 54, No. 2. 1992, pp. 427-449.
[4] Z. F. Guo and M. Shintani, “Nonparametric Lag Selection for Addi-tive Models,” Economics Letters, Vol. 2, No. 2, 2011, pp. 131-134. doi:10.1016/j.econlet.2011.01.014
[5] O. B. Linton and J. P. Nielsen, “A Kernel Method of Estimating Structured Non-parametric Regression Based on Marginal Integration,” Bio-metrika, Vol. 82, No. 1, 1995, pp. 93-100. doi:10.1093/biomet/82.1.93
[6] S. Sperlich, O. B. Linton and W. H?rdle, “Integration and Backfitting Methods in Addi-tive Models-Finite Sample Properties and Comparison,” Test, Vol. 8, No. 2, 1999, pp. 419-458. doi:10.1007/BF02595879
[7] E. Mammen, O. B. Linton and J. P. Nielsen, “The Existence and Asymptotic Properties of a Backfitting Projection Algorithm under Weak Conditions,” Annals of Statistics, Vol. 27, No. 5, 1999, pp. 1443-1490. doi:10.1214/aos/1017939137
[8] J. P. Nielsen and S. Sperlich, “Smoothing Backfitting in Practice,” Journal of the Royal Statistical Society Series B, Vol. 67, No. 1, 2005, pp. 43-61. doi:10.1111/j.1467-9868.2005.00487.x

  
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