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Semiparametric estimation and testing of the trend of temperature series

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  • Jiti Gao
  • Kim Hawthorne

Abstract

The application of a partially linear model to global and hemispheric temperature series is proposed. Partially linear modelling allows the trend to take a very general form rather than imposing the restriction of linearity seen in existing studies. The removal of the linearity restriction is based on the fact that it is well accepted that a significant trend is present in global temperature series. The model will allow for the data to "speak for themselves" with regard to the form of the trend. The results initially reveal that a linear trend does not approximate well the behaviour of global or hemispheric temperature series. This is further confirmed through a formal testing procedure. Copyright Royal Economic Society 2006

Suggested Citation

  • Jiti Gao & Kim Hawthorne, 2006. "Semiparametric estimation and testing of the trend of temperature series," Econometrics Journal, Royal Economic Society, vol. 9(2), pages 332-355, July.
  • Handle: RePEc:ect:emjrnl:v:9:y:2006:i:2:p:332-355
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    Citations

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    Cited by:

    1. Tingting Cheng & Jiti Gao & Xibin Zhang, 2015. "Bayesian Bandwidth Estimation In Nonparametric Time-Varying Coefficient Models," Monash Econometrics and Business Statistics Working Papers 3/15, Monash University, Department of Econometrics and Business Statistics.
    2. Chen, Jia & Gao, Jiti & Li, Degui, 2012. "Semiparametric trending panel data models with cross-sectional dependence," Journal of Econometrics, Elsevier, vol. 171(1), pages 71-85.
    3. Jia Chen & Jiti Gao, 2014. "Semiparametric Model Selection in Panel Data Models with Deterministic Trends and Cross-Sectional Dependence," Monash Econometrics and Business Statistics Working Papers 15/14, Monash University, Department of Econometrics and Business Statistics.
    4. Atak, Alev & Linton, Oliver & Xiao, Zhijie, 2011. "A semiparametric panel model for unbalanced data with application to climate change in the United Kingdom," Journal of Econometrics, Elsevier, vol. 164(1), pages 92-115, September.
    5. Javier Hidalgo & Jungyoon Lee, 2014. "A Cusum Test of Common Trends in Large Heterogeneous Panels," STICERD - Econometrics Paper Series 576, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    6. Patrick Saart & Jiti Gao & Nam Hyun Kim, 2014. "Semiparametric methods in nonlinear time series analysis: a selective review," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 26(1), pages 141-169, March.
    7. Yoosoon Chang & Robert K. Kaufmann & Chang Sik Kim & J. Isaac Miller & Joon Y. Park & Sungkeun Park, 2015. "Evaluating trends in time series of distributions: A spatial fingerprint of human effects on climate," Working Papers 1622, Department of Economics, University of Missouri, revised 19 Dec 2016.
    8. Zhang, Ting, 2015. "Semiparametric model building for regression models with time-varying parameters," Journal of Econometrics, Elsevier, vol. 187(1), pages 189-200.
    9. repec:cep:stiecm:/2014/576 is not listed on IDEAS
    10. Yonghui Zhang & Liangjun Su & Peter C. B. Phillips, 2012. "Testing for common trends in semiā€parametric panel data models with fixed effects," Econometrics Journal, Royal Economic Society, vol. 15(1), pages 56-100, February.
    11. Jia Chen & Degui Li & Jiti Gao, 2013. "Non- and Semi-Parametric Panel Data Models: A Selective Review," Monash Econometrics and Business Statistics Working Papers 18/13, Monash University, Department of Econometrics and Business Statistics.
    12. Gao, Jiti, 2007. "Nonlinear time series: semiparametric and nonparametric methods," MPRA Paper 39563, University Library of Munich, Germany, revised 01 Sep 2007.
    13. repec:hal:journl:peer-00844810 is not listed on IDEAS
    14. repec:ecr:col070:42012 is not listed on IDEAS
    15. Wang, Xiaoguang & Lu, Dawei & Song, Lixin, 2013. "Statistical inference for partially linear stochastic models with heteroscedastic errors," Computational Statistics & Data Analysis, Elsevier, vol. 66(C), pages 150-160.

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