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Functional coefficient seasonal time series models with an application of Hawaii tourism data

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  • Xialu Liu
  • Zongwu Cai
  • Rong Chen

Abstract

In this article, motivated by an analysis of the monthly number of tourists visiting Hawaii, we propose a new class of nonparametric seasonal time series models under the framework of the functional coefficient model. The coefficients change over time and consist of the trend and seasonal components to characterize seasonality. A local linear approach is developed to estimate the nonparametric trend and seasonal effect functions. The consistency of the proposed estimators is obtained without specifying the error distribution and the asymptotic normality of the proposed estimators is established under the $$\alpha $$ α -mixing conditions. A consistent estimator of the asymptotic variance is also provided. The proposed methodologies are illustrated by two simulated examples and the model is applied to characterizing the seasonality of the monthly number of tourists visiting Hawaii. Copyright Springer-Verlag Berlin Heidelberg 2015

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  • Xialu Liu & Zongwu Cai & Rong Chen, 2015. "Functional coefficient seasonal time series models with an application of Hawaii tourism data," Computational Statistics, Springer, vol. 30(3), pages 719-744, September.
    • Handle: RePEc:spr:compst:v:30:y:2015:i:3:p:719-744
    DOI: 10.1007/s00180-015-0574-x
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    References listed on IDEAS

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    1. Ostap Okhrin & Stefan Trück, 2015. "Editorial to the special issue on Applicable semiparametrics of computational statistics," Computational Statistics, Springer, vol. 30(3), pages 641-646, September.

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