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A Nonparametric Panel Model for Climate Data with Seasonal and Spatial Variation

Author

Listed:
  • Gao, J.
  • Linton, O.
  • Peng, B.

Abstract

In this paper, we consider a panel data model which allows for heterogeneous time trends at different locations. We propose a new estimation method for the panel data model before we establish an asymptotic theory for the proposed estimation method. For inferential purposes, we develop a bootstrap method for the case where weak correlation presents in both dimensions of the error terms. We examine the ï¬ nite–sample properties of the proposed model and estimation method through extensive simulated studies. Finally, we use the newly proposed model and method to investigate rainfall, temperature and sunshine data of U.K. respectively. Overall, we ï¬ nd the weather of winter has changed dramatically over the past ï¬ fty years. Changes may vary with respect to locations for the other seasons.

Suggested Citation

  • Gao, J. & Linton, O. & Peng, B., 2022. "A Nonparametric Panel Model for Climate Data with Seasonal and Spatial Variation," Janeway Institute Working Papers 2215, Faculty of Economics, University of Cambridge.
    • Handle: RePEc:cam:camjip:2215
    Note: obl20
    as

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    File URL: https://www.janeway.econ.cam.ac.uk/working-paper-pdfs/jiwp2215.pdf
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    References listed on IDEAS

    as
    1. Chen, Jia & Gao, Jiti & Li, Degui, 2012. "A New Diagnostic Test For Cross-Section Uncorrelatedness In Nonparametric Panel Data Models," Econometric Theory, Cambridge University Press, vol. 28(5), pages 1144-1163, October.
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    More about this item

    Keywords

    Bootstrap method; Interactive ï¬ xed–effect; Panel rainfall data; Time trend;
    All these keywords.

    JEL classification:

    • Q50 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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