Nonparametric lag selection for nonlinear additive autoregressive models
The lag selection procedure based on the final prediction error (FPE) is investigated when the additive structure is a priori known in the nonparametric autoregression. The consistency of the lag selection is proved, followed by the finite sample simulation results.
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- Oliver Linton & E. Mammen & J. Nielsen, 1997.
"The Existence and Asymptotic Properties of a Backfitting Projection Algorithm Under Weak Conditions,"
Cowles Foundation Discussion Papers
1160, Cowles Foundation for Research in Economics, Yale University.
- Enno Mammen & Oliver Linton & J Nielsen, 2000. "The existence and asymptotic properties of a backfitting projection algorithm under weak conditions," LSE Research Online Documents on Economics 2315, London School of Economics and Political Science, LSE Library.
- Oliver Linton & Enno Mammen & N Nielsen, 2000. "The Existence and Asymptotic Properties of a Backfitting Projection Algorithm under Weak Conditions," STICERD - Econometrics Paper Series 386, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
- Oliver Linton & E. Mammen & J. Nielsen, 1999. "The existence and asymptotic properties of a backfitting projection algorithm under weak conditions," LSE Research Online Documents on Economics 300, London School of Economics and Political Science, LSE Library.
- Martins-Filho, Carlos & yang, ke, 2007. "Finite sample performance of kernel-based regression methods for non-parametric additive models under common bandwidth selection criterion," MPRA Paper 39295, University Library of Munich, Germany.
- Enno Mammen, 2003. "Generalised structured models," Biometrika, Biometrika Trust, vol. 90(3), pages 551-566, September.
- Hirotugu Akaike, 1969. "Fitting autoregressive models for prediction," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 21(1), pages 243-247, December. Full references (including those not matched with items on IDEAS)
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