WORKING PAPERS
(please let me know it if you have any problems downloading any of them!!!!)
"Long-term climate forecasts: A Heterogeneous Future" (with Lola Gadea) (WP Economics Dept, UC3M, 2025) (Submitted to IJF)
Abstract:
Climate is a long-term issue, and as such, climate forecasts should be designed with a long-term perspective. These forecasts are critical for crafting mitigation policies aimed
at achieving one of the primary objectives of the Paris Climate Agreement (PCA) and for designing adaptation strategies to alleviate the adverse effects of climate change.
Furthermore, they serve as indispensable tools for assessing climate risks and guiding the green transition effectively. This paper introduces a straightforward method for
generating long-term temperature density forecasts using observational data, leveraging the realized quantile methodology developed by Gadea and Gonzalo (JoE, 2020).
This methodology transforms unconditional quantiles into time series objects. The resulting forecasts complement those produced by physical climate models, which primarily
focus on average temperature values. By contrast, our density forecasts capture broader distributional characteristics, including spatial disparities that are often obscured
in mean-based projections. The proposed approach involves conducting an out of-sample forecast model competition and integrating the forecasts from the resulting
Pareto-superior models. This method reduces dependency on any single forecast model, enhancing the robustness of the results. Additionally, recognizing climate change as a
non-uniform phenomenon, our approach emphasizes the importance of analyzing climate data from a regional perspective, providing differentiated predictions to address
the complexities of a heterogeneous future. This regional focus underscores the necessity of accounting for spatial disparities to better assess risks and develop effective
policies for mitigation, adaptation, and compensation. Finally, this paper advocates that future climate agreements and policymakers should prioritize analyzing the entire
temperature distribution rather than focusing solely on average values
"Regional Heterogeneity and Warming Dominance in the
United States" (with Lola Gadea) (WP Economics Dept, UC3M, 2024) (Submitted to PLOS CLIMATE)
Abstract:
Climate change exhibits both spatial and temporal heterogeneity, requiring a dual approach to mitigation and adaptation policies
that addresses global and local challenges. Accurately measuring this heterogeneity is essential for attribution and impact analysis.
This study introduces straightforward quantitative tools to detect the presence of warming, characterize warming types, and compare
warming processes across regions through the concept of “warming dominance”. These analyses are presented in a robust, testable
format, considering the entire temperature distribution rather than just the mean. Applied to the United States from 1950 to 2021,
the results indicate that while 50% of states do not exhibit warming in terms of mean temperature, warming is detectable in 84% of
states when different quantiles are considered. Clear heterogeneity in warming patterns is observed: some states show no warming, others exhibit stronger warming in lower quantiles, some in upper quantiles,
and a few demonstrate uniform warming across the temperature distribution.
The paper concludes by identifying which states exhibit warming dominance over others and which states are subject to such dominance.
"Estimation of Characteristics-based Quantile Factor Models" (Appendix) (with Liang Chen, Juanjo Dolado and Haozi Pan) (June, 2024) (R&R JoE).
Abstract:
This paper studies the estimation of characteristics-based quantile factor models where the factor loadings are unknown functions of observed individual characteristics, while the
idiosyncratic error terms are subject to conditional quantile restrictions. We propose a threestage estimation procedure that is easily implementable in practice and has nice properties.
The convergence rates, the limiting distributions of the estimated factors and loading functions, plus a consistent selection criterion for the number of factors at each quantile are
derived under general conditions. The proposed estimation methodology is shown to work satisfactorily when: (i) the idiosyncratic errors have heavy tails, (ii) the time dimension
of the panel dataset is not large, and (iii) the number of factors exceeds the number of characteristics. Finite sample simulations and an empirical application aimed at estimating
the loading functions of the daily returns of a large panel of S&P500 index securities help illustrate these properties.
" Warming Dominance"" (with Lola Gadea) (March 2025) (Submitted to Nature).
Abstract:
In Revision!!!!...Stay tuned!!
"Polar Warming" (with Lola Gadea) (This version August 2024) (R&R in AE)
Abstract:
In Revision!!!!...Stay tuned!!
" PaleoCooling and PaleoWarming" (with Andrey Ramos) (2022).
"Threshold Stochastic Unit Root Models" (with Raquel Montesion and Junji Peng) (2018)
Abstract:
"Co-Summability" (with Vanessa Bereneguer-Rico) (PDF + Code available upon request) (This version is October 2014)
A Short Informal Abstract:
Co-integration plays a fundamental role in the econometric analysis of linear relationships
among persistent economic time series. Nonetheless, nonlinearities are often encountered in
modern macroeconometric models that account for more ‡exible relationships. In a nonlinear
world, however, the concepts order of integration and co-integration are not readily applicable.
The inherent linearity in the order of integration idea invalidates its use to characterize nonlinear
persistent and/or nonstationary processes and this, in turn, implies that co-integration cannot
be directly extended to study nonlinear relationships.
To overcome these hindrances, Berenguer-Rico and Gonzalo (2014) formalized the concept
order of summability of a stochastic process, which generalizes the order of integration idea to
nonlinear time series. In this paper, the order of summability is used to extend co-integration to
non-linear models. Speci…cally, we formalise the idea of co-summability and propose a residual-
based statistic to test for it. The statistic can also be seen as a misspeci…cation testing procedure
and is based on the order of summability of the error term. The performance of the test is studied
via Monte Carlo experiments. Finally, the practical strength of co-summability theory is shown
through two empirical applications. In particular, asymmetric preferences of central bankers and
the environmental Kuznets curve hypothesis are studied through the lens of co-summability.
"SHOCKS (Can we identify them? YES, WE CAN)" (with Oscar Martinez)
A Short Informal Abstract:
At time "t" there is a shock e_t. This shock can be big or small, positive or negative, blue or red, may
have been produced when the economic is recession or expansion, when inflation is high or small,
when Barcelona soccer team wins or looses, etc. In this paper, via a new Threshold Moving Average Model,
we show how to identify and test if these type of shocks are transitory or permanent.
"Threshold Integrated Moving Average Models " (with Oscar Martínez) (Slides)
A Short Informal Abstract: We are revising it.......stay tuned!!!"Econometric Implications of Non Exact Present Value Relations" (PDF) (with Martín González) (last version Sept-1998)
"Threshold Unit Root Processes" (PDF) (with Martín González) (last version 1998)
PAPERS UNDER CONSTRUCTION
Many....some of them will be available soon if time teaching constraints allow me to do it...
