This paper introduces a time-series methodology to quantify heterogeneity in the dynamics of the unconditional temperature distribution and its association with climate drivers. By recognizing that temperature unconditional quantiles represent temperatures at different locations or seasons, I establish an equivalence between a structural one-dimensional energy balance model and a reduced-form vector error correction model for the mean and quantiles of temperature together with total radiative forcing, including greenhouse gases. The framework is used to estimate long-run responses, generate density forecasts, produce scenario-based projections, extract the common trend behind warming, and identify distributional climate shocks. Evidence from station-level temperature records for 1880-2021 reveals strong climate heterogeneity at global, hemispheric, and continental levels.

Accurate tracking of anthropogenic carbon dioxide emissions is crucial for shaping climate policies and meeting global decarbonization targets. This paper introduces a panel nowcasting framework to obtain higher-frequency predictions of state-level per-capita energy consumption and CO2 emissions growth in the United States. The approach combines a panel MIDAS specification for energy consumption with a bridge equation that links emissions growth to those nowcasts using panel quantile regression methods. A pseudo out-of-sample exercise for 2009-2018 shows gains in predictive accuracy relative to a historical benchmark and supports the use of higher-frequency indicators for more reliable density estimates.

This paper examines the heterogeneous effects of precipitation shocks on poverty in Ecuador. Using gridded monthly precipitation data for 2007-2021 merged with household information from ENEMDU, the paper constructs measures of excess and deficit rainfall at the parish level. The results show that precipitation shocks significantly affect poverty, with stronger adverse effects in primary-sector employment and more mixed effects in secondary and tertiary activities. Household income and labor earnings emerge as key transmission channels, and the effects vary by formality, urban status, and self-employment.

This paper introduces a methodology to examine the macroeconomic effects of temperature distributional shocks, meaning shocks to the average and quantiles of the temperature process. The shocks are defined as persistent deviations from the long-run trend and are obtained using the Hamilton filter. Because the shocks are correlated, a factor model is used to extract the main sources of common variation. The analysis for the global economy, the United States, and the euro area identifies three orthogonal factor shocks with meaningful macroeconomic effects, including channels that are not captured by average temperature alone.