Climate Policy Uncertainty and the Forecastability of Inflation

We investigate the predictive content of climate policy uncertainty (CPU) for forecasting the inflation rate of the United States (US) over the monthly period of 1987:05 to 2024:11. We evaluate the performance of our proposed CPU-based predictive model, estimated via the Feasible Quasi Generalized Least Squares (FQGLS) approach, against a historical average benchmark model, with the FQGLS technique adopted to account for heteroscedasticity and autocorrelation in the data. We find statistical evidence in favor of a CPU-based model relative to the benchmark, as well as in case of an extended model involving physical risks of climate change and financial and macroeconomic factors, extracted from a large data set, when CPU is included. The predictive superiority of climate policy-related uncertainties relative to the historical mean continues to be robust under alternative local and global metrics of CPU, as well as in a mixed-frequency set-up, given the availability of high-frequency (weekly) CPU data. Moreover, the importance of local- and global-CPUs is also found to hold in forecasting the inflation rates of 11 other advanced and emerging countries in a statistically significant manner compared to the historical average model. Though across all the 12 economies, own- and global-CPUs perform equally well in forecasting the respective inflation rates. The general importance of uncertainties surrounding policy decisions to tackle climate change in shaping the future path of inflation, understandably, carries implications for the monetary authority.