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New asymptotics applied to functional coefficient regression and climate sensitivity analysis

Author

Listed:
  • Qiying Wang

    (University of Sydney)

  • Peter C. B. Phillips

    (Cowles Foundation, Yale University)

  • Ying Wang

    (Renmin University of China)

Abstract

A general asymptotic theory is established for sample cross moments of nonstationary time series, allowing for long range dependence and local unit roots. The theory provides a substantial extension of earlier results on nonparametric regression that include near-cointegrated nonparametric regression as well as spurious nonparametric regression. Many new models are covered by the limit theory, among which are functional coefficient regressions in which both regressors and the functional covariate are nonstationary. Simulations show finite sample performance matching well with the asymptotic theory and having broad relevance to applications, while revealing how dual nonstationarity in regressors and covariates raises sensitivity to bandwidth choice and the impact of dimensionality in nonparametric regression.

Suggested Citation

  • Qiying Wang & Peter C. B. Phillips & Ying Wang, 2023. "New asymptotics applied to functional coefficient regression and climate sensitivity analysis," Cowles Foundation Discussion Papers 2365, Cowles Foundation for Research in Economics, Yale University.
    • Handle: RePEc:cwl:cwldpp:2365
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    File URL: https://cowles.yale.edu/sites/default/files/2023-07/d2365.pdf
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    References listed on IDEAS

    as
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    11. Magnus, Jan R. & Melenberg, Bertrand & Muris, Chris, 2011. "Global Warming and Local Dimming: The Statistical Evidence," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 452-464.
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    22. Wang, Qiying & Phillips, Peter C.B., 2009. "Asymptotic Theory For Local Time Density Estimation And Nonparametric Cointegrating Regression," Econometric Theory, Cambridge University Press, vol. 25(3), pages 710-738, June.
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