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A semiparametric panel model for unbalanced data with application to climate change in the United Kingdom

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  • Atak, Alev
  • Linton, Oliver
  • Xiao, Zhijie

Abstract

This paper is concerned with developing a semiparametric panel model to explain the trend in UK temperatures and other weather outcomes over the last century. We work with the monthly averaged maximum and minimum temperatures observed at the twenty six Meteorological Office stations. The data is an unbalanced panel. We allow the trend to evolve in a nonparametric way so that we obtain a fuller picture of the evolution of common temperature in the medium timescale. Profile likelihood estimators (PLE) are proposed and their statistical properties are studied. The proposed PLE has improved asymptotic property comparing the sequential two-step estimators. Finally, forecasting based on the proposed model is studied.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 164 (2011)
Issue (Month): 1 (September)
Pages: 92-115

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Handle: RePEc:eee:econom:v:164:y:2011:i:1:p:92-115

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Web page: http://www.elsevier.com/locate/jeconom

Related research

Keywords: Global warming Kernel estimation Semiparametric Trend analysis;

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References

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  1. Lima, Luiz Renato Regis de Oliveira & Issler, João Victor, 2008. "A Panel Data Approach to Economic Forecasting: The Bias-Corrected Average Forecast," Economics Working Papers (Ensaios Economicos da EPGE) 668, FGV/EPGE Escola Brasileira de Economia e Finanças, Getulio Vargas Foundation (Brazil).
  2. Campbell, Sean D. & Diebold, Francis X., 2004. "Weather forecasting for weather derivatives," CFS Working Paper Series 2004/10, Center for Financial Studies (CFS).
  3. repec:att:wimass:9001 is not listed on IDEAS
  4. 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, 07.
  5. Rice, John, 1986. "Convergence rates for partially splined models," Statistics & Probability Letters, Elsevier, vol. 4(4), pages 203-208, June.
  6. Hoogstrate, Andre J & Palm, Franz C & Pfann, Gerard A, 2000. "Pooling in Dynamic Panel-Data Models: An Application to Forecasting GDP Growth Rates," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(3), pages 274-83, July.
  7. Pateiro-López, Beatriz & González-Manteiga, Wenceslao, 2006. "Multivariate partially linear models," Statistics & Probability Letters, Elsevier, vol. 76(14), pages 1543-1549, August.
  8. Newey, Whitney K & Powell, James L & Walker, James R, 1990. "Semiparametric Estimation of Selection Models: Some Empirical Results," American Economic Review, American Economic Association, vol. 80(2), pages 324-28, May.
  9. Miguel A. Delgado & Thanasis Stengos, 1990. "Semiparametric Specification Testing," Working Papers 778, Queen's University, Department of Economics.
  10. Robinson, Peter M, 1988. "Root- N-Consistent Semiparametric Regression," Econometrica, Econometric Society, vol. 56(4), pages 931-54, July.
  11. Lee, Lung-fei & Rosenzweig, Mark R. & Pitt, Mark M., 1997. "The effects of improved nutrition, sanitation, and water quality on child health in high-mortality populations," Journal of Econometrics, Elsevier, vol. 77(1), pages 209-235, March.
  12. Ahn, Hyungtaik & Powell, James L., 1993. "Semiparametric estimation of censored selection models with a nonparametric selection mechanism," Journal of Econometrics, Elsevier, vol. 58(1-2), pages 3-29, July.
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Cited by:
  1. Jia Chen & Jiti Gao & Degui Li, 2011. "Semiparametric Trending Panel Data Models with Cross-Sectional Dependence," Monash Econometrics and Business Statistics Working Papers 15/11, Monash University, Department of Econometrics and Business Statistics.
  2. Yonghui Zhang & Liangjun Su & Peter C.B. Phillips, 2011. "Testing for Common Trends in Semiparametric Panel Data Models with Fixed Effects," Cowles Foundation Discussion Papers 1832, Cowles Foundation for Research in Economics, Yale University.
  3. Lena Korber & Oliver Linton & Michael Vogt, 2013. "A semiparametric model for heterogeneous panel data with fixed effects," CeMMAP working papers CWP02/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  4. 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.

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