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A Semiparametric Panel Model for unbalanced data with Application to Climate Change in the United Kingdom

  • Alev Atak


    (Queen Mary, University of London)

  • Oliver Linton


    (London School of Economics)

  • Zhijie Xiao


    (Boston College)

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 the sequential two-step estimators. Finally, forecasting based on the proposed model is studied.

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Paper provided by Boston College Department of Economics in its series Boston College Working Papers in Economics with number 762.

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Date of creation: 01 Sep 2010
Date of revision:
Handle: RePEc:boc:bocoec:762
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  1. Sean D. Campbell & Francis X. Diebold, 2002. "Weather Forecasting for Weather Derivatives," Center for Financial Institutions Working Papers 02-42, Wharton School Center for Financial Institutions, University of Pennsylvania.
  2. 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.
  3. 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.
  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. Issler, João Victor & Lima, Luiz Renato, 2009. "A panel data approach to economic forecasting: The bias-corrected average forecast," Journal of Econometrics, Elsevier, vol. 152(2), pages 153-164, October.
  6. Robinson, Peter M, 1988. "Root- N-Consistent Semiparametric Regression," Econometrica, Econometric Society, vol. 56(4), pages 931-54, July.
  7. Miguel A. Delgado & Thanasis Stengos, 1990. "Semiparametric Specification Testing," Working Papers 778, Queen's University, Department of Economics.
  8. 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.
  9. Pateiro-López, Beatriz & González-Manteiga, Wenceslao, 2006. "Multivariate partially linear models," Statistics & Probability Letters, Elsevier, vol. 76(14), pages 1543-1549, August.
  10. Rice, John, 1986. "Convergence rates for partially splined models," Statistics & Probability Letters, Elsevier, vol. 4(4), pages 203-208, June.
  11. 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.
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