Forecasting global temperatures by exploiting cointegration with radiative forcing - Luca Benati (University of Bern, Department of Economics, 2023)
I use Bayesian VARs to forecast global temperatures anomalies until the end of the XXI century by exploiting their cointegration with the Joint Radiative Forcing (JRF) of the drivers of climate change. Under a 'no change' scenario, the most favorable median forecast predicts the land temperature anomaly to reach 5.6 Celsius degrees in 2100. Forecasts conditional on alternative paths for the JRF show that, given the extent of uncertainty, bringing climate change under control will require to bring the JRF back to the level reached in the early years of the XXI century. From a methodological point of view, my evidence suggests that previous cointegration-based studies of climate change suffer from model mis-specification.
Keywords: E2; E3; 330; Climate change; Bayesian VARs; cointegration; forecasting; conditional forecasts