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 (1D-EBM) and a statistical reduced-form Vector Error Correction Model (VECM) for a range of distributional characteristics of temperature—mean and quantiles—and total radiative forcing, including radiative forcing from anthropogenic greenhouse gases (GHGs). The VECM is estimated employing time-series methods agnostic about the type of trends (stochastic or deterministic) in climate data, and is utilized to produce the following outcomes of practical interest for economic analyses: i) estimation of long-run responses of temperature distribution to changes in GHGs, ii) long-term temperature density forecasts, iii) conditional projections of temperature distribution under different scenarios of future GHGs emissions, iv) extraction of the common-trending component behind the existing warming trend, and v) identification of distributional climate shocks and impulse-response analysis. An empirical study using station-level temperature records (1880–2021) reveals strong climate heterogeneity at global, hemispheric, and continental (Europe) scales, with important potential implications for damage analysis and integrated assessment modeling. A deeper understanding of global warming dynamics is crucial for informing more efficient adaptation and mitigation policies.

This paper proposes a method to estimate quantile regression models with multiple fixed effects. We extend the quantile–via–moments estimator of Machado and Santos Silva (2019) and suggest a computationally efficient Frisch–Waugh–Lovell residualization to partial out additive fixed effects in both the location and scale equations. A unified influence-function inference framework is derived, accommodating heteroskedasticity-robust, clustered, and feasible GLS standard errors. Monte Carlo simulations provide strong support for the validity of the proposed procedure in applications with multi-way unobserved heterogeneity and intra-cluster correlated disturbances. An empirical application to Climate Growth-at-Risk illustrates how temperature shocks affect the conditional distribution of macroeconomic outcomes in a panel of 194 countries. Our findings suggest that in low income countries, downside risks to growth are more strongly linked to temperature shocks than the central tendency or upside risks.

Accurate tracking of anthropogenic carbon dioxide (CO2) emissions is crucial for shaping climate policies and meeting global decarbonization targets. However, energy consumption and emissions data are released annually and with substantial publication lags, hindering timely decision-making. This paper introduces a panel nowcasting framework to produce higher-frequency predictions of the state-level growth rate of per-capita energy consumption and CO2 emissions in the United States (U.S.). Our approach employs a panel mixed-data sampling (MIDAS) model to predict per-capita energy consumption growth, considering quarterly personal income, monthly electricity consumption, and a weekly economic conditions index as predictors. A bridge equation linking per-capita CO2 emissions growth with the nowcasts of energy consumption is estimated using panel quantile regression methods. A pseudo out-of-sample study (2009-2018), simulating the real-time data release calendar, confirms the improved accuracy of our nowcasts with respect to a historical benchmark. Our results suggest that by leveraging the availability of higher-frequency indicators, we not only enhance predictive accuracy for per-capita energy consumption growth but also provide more reliable estimates of the distribution of CO2 emissions growth.

This paper examines how precipitation shocks affect poverty in Ecuador. Using gridded monthly precipitation data for 2007-2024, we construct parish-level measures of excess and deficit precipitation and link them to household socioeconomic data from the National Survey of Employment, Unemployment, and Underemployment (ENEMDU). Our empirical estimates show that both excess and deficit precipitation increase the likelihood of poverty among workers in the primary sector (agriculture and mining), consistent with disruptions to agricultural production and local infrastructure. By contrast, in the tertiary sector (commerce and services), precipitation shocks lower the probability of poverty, plausibly reflecting heightened demand for health and social assistance, transportation, logistics, and related services during emergency response and recovery phases. We identify per-capita labor income and per-capita final household income as primary transmission channels, with magnitudes conditioned by formality status, location (urban versus rural), self-employment, and the recurrence of shocks. These findings underscore the potential of targeted sector-specific policies to mitigate the poverty impacts of weather shocks.

This paper introduces a methodology to identify temperature distributional shocks, i.e., shocks capturing shifts not only in the average but also across different quantiles of the temperature process. Using data for the globe and a panel of 21 economies, we consistently recover three types of local and global temperature distributional shocks and estimate their macroeconomic impacts on output and total factor productivity growth. The first type of shock captures a classic distributional shift and yields economic responses consistent with studies that assume the average temperature as a sufficient statistic for climate change. Our key contribution is to uncover two additional types of shocks reshaping the temperature distribution: (i) a variability-shock that moves mid-lower and mid-upper quantiles in opposite directions, and (ii) an extremes-shock that shifts the tails relative to the center. These variability and extremes shocks induce macroeconomic responses not documented in standard average-based studies. Our results reveal the value of modeling changes in the whole temperature distribution for climate-macro analysis and carry important implications for social cost of carbon estimation and climate-related risk assessment.