Non- And Semiparametric Identification Of Seasonal Nonlinear Autoregression Models
AbstractNon- or semiparametric estimation and lag selection methods are proposed for three seasonal nonlinear autoregressive models of varying seasonal flexibility. All procedures are based on either local constant or local linear estimation. For the semiparametric models, after preliminary estimation of the seasonal parameters, the function estimation and lag selection are the same as nonparametric estimation and lag selection for standard models. A Monte Carlo study demonstrates good performance of all three methods. The semiparametric methods are applied to German real gross national product and UK public investment data. For these series our procedures provide evidence of nonlinear dynamics.
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Bibliographic InfoArticle provided by Cambridge University Press in its journal Econometric Theory.
Volume (Year): 18 (2002)
Issue (Month): 06 (December)
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Other versions of this item:
- L. Yang & R. Tschernig, 1998. "Non- and Semiparametric Identification of Seasonal Nonlinear Autoregression Models," SFB 373 Discussion Papers 1998,114, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
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- Yang, Lijian & Park, Byeong U. & Xue, Lan & Hardle, Wolfgang, 2006.
"Estimation and Testing for Varying Coefficients in Additive Models With Marginal Integration,"
Journal of the American Statistical Association,
American Statistical Association, vol. 101, pages 1212-1227, September.
- Yang, Lijian & Härdle, Wolfgang & Park, Byeong U., 2002. "Estimation and testing for varying coefficients in additive models with marginal integration," SFB 373 Discussion Papers 2002,75, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
- Lijian Yang & Byeong U. Park & Lan Xue & Wolfgang Härdle, 2005. "Estimation and Testing for Varying Coefficients in Additive Models with Marginal Integration," SFB 649 Discussion Papers SFB649DP2005-047, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
- Gao, Jiti, 2007. "Nonlinear time series: semiparametric and nonparametric methods," MPRA Paper 39563, University Library of Munich, Germany, revised 01 Sep 2007.
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