Working Paper: CEPR ID: DP18114
Authors: Liang Chen; Juan J. Dolado; Jesus Gonzalo; Andrey Ramos
Abstract: Global warming is a non-uniform process across space and time. This opens the door to a heterogeneous relationship between CO2 and temperature that needs to be analyzed going beyond the standard analysis based on mean temperature found in the literature. We revisit this topic through the lenses of a new class of factor models for high-dimensional panel data, labeled Quantile Factor Models (QFM). This technique extracts quantile-dependentfactors 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 whetherthe (detrended) growth rate of CO2 concentrations help predict the underlying factors of the different quantiles of the distribution of (detrended) temperature in the time dimension. We document that predictive association is greater at the lower and medium quantiles than at the upper quantiles and provide some conjectures about what could be behind this nonuniformity. These findings complement recent results in the literature documenting steepertrends in lower temperature levels than in other parts of the spatial distribution
Keywords: Global Warming; CO2 Concentrations; Quantile Factor Models; Predictive Association
JEL Codes: C31; C33; Q54
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| CO2 concentrations (Q54) | temperature fluctuations (E32) |
| CO2 concentrations (Q54) | temperature fluctuations (lower quantiles) (C22) |
| temperature fluctuations (E32) | CO2 concentrations (Q54) |