Skip to main content

ETHNOS_APP

Home • Search • Journals • List 0

Heterogeneous predictive association of CO 2 with global warming

Bibliographic Data

ID9720827
AuthorsLiang Chen (0000-0003-1553-2846, Peking University HSBC Business School China), Juan J Dolado (0000-0003-3790-2457, Universidad Carlos III de Madrid Spain, corresponding author), Jesús Gonzalo (0000-0002-4309-5025, Universidad Carlos III de Madrid Spain), Andrey Ramos (Universidad Carlos III de Madrid Spain)
Year2023
Volume90
Issue360
Pages1397-1421
Publication date2023-10-01
Open AccessYes
TypeARTICLE
VenueEconomica (JOURNAL)
Journal identifiersISSN: 0013-0427 • E-ISSN: 1468-0335
PublisherWiley (PUBLISHER • GB)
DOI10.1111/ecca.12491
OpenAlexW4385250279
LanguageEN
Citations received1
References cited38

Global warming is a non‐uniform process across space and time. This opens the door to a heterogeneous relationship between and temperature that needs to be explored going beyond the standard analysis based on mean temperature. We revisit this topic through the lens of a new class of factor models for high‐dimensional panel data, called quantile factor models. This technique extracts quantile‐dependent factors from the distributions of temperature across a wide range of stable weather stations in the northern and southern hemispheres over 1959–2018. In particular, we test whether the (detrended) growth rate of concentrations helps to predict the underlying factors of the different quantiles of the distribution of (detrended) temperatures in the time dimension. We document that predictive association is greater at the lower and medium quantiles than at the upper quantiles of temperature in all stations, and provide some conjectures about what could be behind this non‐uniformity. These findings complement recent results in the literature documenting steeper trends in lower temperature levels than in other parts of the spatial distribution

Climate change · Climatology · Detrended fluctuation analysis · Dimension (graph theory) · Econometrics · Global warming · Quantile · Range (aeronautics) · Statistics · Atmospheric and Environmental Gas Dynamics · Climate Change Policy and Economics · Environmental Science · Geology · Mathematics · Spatial and Panel Data Analysis

  • Inflation co-movement

    Open Access•Saban Nazlioglu, Sinem Pinar Gurel et al.•Empirical Economics•2025

  • Quantile Regression

    Roger Koenker•Quantile Regression•2005

  • Robust Statistics

    Open Access•Peter J Huber•Robust statistics•1981

  • Global Carbon Budget 2021

    Open Access•Pierre Friedlingstein, Matthew W Jones et al.•Earth System Science Data•2022

  • An Assessment of Earth's Climate Sensitivity Using Multiple Lines of Evidence

    Open Access•Steven C Sherwood, M J Webb et al.•Reviews of Geophysics•2020

  • Determining the Number of Factors in Approximate Factor Models

    Open Access•Jushan Bai, Serena Ng•Econometrica•2002

  • Inferential Theory for Factor Models of Large Dimensions

    Open Access•Jushan Bai•Econometrica•2003

  • XXXI. On the influence of carbonic acid in the air upon the temperature of the ground

    Svante Arrhénius•The London, Edinburgh, and Dublin…•1896

  • Confidence Intervals for Diffusion Index Forecasts and Inference for Factor-Augmented Regressions

    Open Access•Jushan Bai, Serena Ng•Econometrica•2006

Citations per year1
Citation span2025 - 2025 (1)
Highly citedNo

Tools

Open DOIOpen Access
Ethnos_APP • Open Source Project • MIT License • Frontend v2.0.0 • Privacy and Cookies • API Documentation: api.ethnos.app/docs • API Source Code: GitHub • DOI: 10.5281/zenodo.17049435 • Frontend Source Code: GitHub • DOI: 10.5281/zenodo.17050053 • cruz.rio.br • Expectantes Misericordiae