Evaluating trends in time series of distributions: a spatial fingerprint of human effects on climate

DOI10.1016/J.JECONOM.2019.05.014zbMath1456.62260OpenAlexW2598915898MaRDI QIDQ2280619

Sungkeun Park, Joon Y. Park, J. Isaac Miller, Robert K. Kaufmann, Yoosoon Chang, Chang Sik Kim

Publication date: 19 December 2019

Published in: Journal of Econometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jeconom.2019.05.014

zbMATH Keywords

temperature distributionclimate changefunctional unit rootsglobal temperature trends

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to environmental and related topics (62P12) Climate science and climate modeling (86A08)

Related Items (5)

Global temperatures and greenhouse gases: a common features approach ⋮ INFERENCE ON THE DIMENSION OF THE NONSTATIONARY SUBSPACE IN FUNCTIONAL TIME SERIES ⋮ Functional principal component analysis for cointegrated functional time series ⋮ Bootstrap procedures for detecting multiple persistence shifts in heteroskedastic time series ⋮ Time-varying cointegration and the Kalman filter

Cites Work

• Global space-time models for climate ensembles
• Functional regression of continuous state distributions
• Nonstationary covariance models for global data
• Kernel density estimation for spatial processes: The \(L_{1}\) theory
• Linear processes in function spaces. Theory and applications
• Energy balance climate models and general equilibrium optimal mitigation policies
• Detection and attribution of climate change through econometric methods
• Likelihood-Based Inference in Cointegrated Vector Autoregressive Models
• Cointegrated Linear Processes in Hilbert Space
• Dynamic Functional Principal Components
• Semiparametric estimation and testing of the trend of temperature series
• Rejoinder
• Nonstationarity in time series of state densities

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