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Seasonality analysis of time series in partial linear models

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  • Q. Shao

Abstract

Seasonality analysis is one of the classic topics in time series. This paper studies techniques for seasonality analysis when the trend function is unspecified. The asymptotic properties of the semiparametric estimators are derived, and an estimation algorithm is provided. The techniques are applied to making inference for the monthly global land–ocean temperature anomaly indexes.

Suggested Citation

  • Q. Shao, 2009. "Seasonality analysis of time series in partial linear models," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 21(7), pages 827-837.
    • Handle: RePEc:taf:gnstxx:v:21:y:2009:i:7:p:827-837
    DOI: 10.1080/10485250903108391
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    References listed on IDEAS

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    1. Ferreira, Eva & Nunez-Anton, Vicente & Rodriguez-Poo, Juan, 2000. "Semiparametric approaches to signal extraction problems in economic time series," Computational Statistics & Data Analysis, Elsevier, vol. 33(3), pages 315-333, May.
    2. Li, Qi, 2000. "Efficient Estimation of Additive Partially Linear Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 41(4), pages 1073-1092, November.
    3. Hart, Jeffrey D., 1989. "Differencing as an approximate de-trending device," Stochastic Processes and their Applications, Elsevier, vol. 31(2), pages 251-259, April.
    4. Hardle, Wolfgang & LIang, Hua & Gao, Jiti, 2000. "Partially linear models," MPRA Paper 39562, University Library of Munich, Germany, revised 01 Sep 2000.
    5. Gao, Jiti, 1994. "Asymptotic theory for partly linear models," MPRA Paper 40452, University Library of Munich, Germany, revised 02 Dec 1994.
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