Special Issue "Econometric Analysis of Climate Change"

A special issue of Econometrics (ISSN 2225-1146).

Deadline for manuscript submissions: 31 October 2021.

Special Issue Editor

Claudio Morana
E-Mail Website
Guest Editor
Dipartimento Di Economia, Metodi Quantitativi E Strategie Di Impresa, Italy
Interests: linear and nonlinear large-scale time series models; macro, financial and climate change econometrics; the macro-finance interface and boom-bust macro-financial cycles

Special Issue Information

Dear Colleagues,

This Special Issue aims to promote an interdisciplinary approach to the detection and attribution of climate change; the cross-fertilization between climate science, economics, and econometrics; and econometric estimates of climate impacts and policy evaluation. We solicit the submission of papers whose novelty stems from the development and introduction of new econometric models of climate change, and we invite papers using econometric methods to analyze climate data, as well as economic, financial, and econometric studies of climate impacts. We particularly welcome submissions in the field of climatology highlighting interesting statistical challenges to which econometric methods can contribute.

Prof. Claudio Morana
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the Special Issue website. Research articles and review articles, as well as short communications, are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on the website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for the submission of manuscripts is available on the Instructions for Authors page. Econometrics is an international, peer-reviewed, open access, quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. There are no article processing charges (APCs) for publication in this Special Issue. Submitted papers should be well formatted and use proper English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • econometrics
  • climate change
  • environmental, economic, and financial implications of climate change

Published Papers (8 papers)

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Research

Open AccessFeature PaperArticle
Monitoring Cointegrating Polynomial Regressions: Theory and Application to the Environmental Kuznets Curves for Carbon and Sulfur Dioxide Emissions
Econometrics 2021, 9(1), 12; https://doi.org/10.3390/econometrics9010012 - 13 Mar 2021
Viewed by 343
Abstract
This paper develops residual-based monitoring procedures for cointegrating polynomial regressions (CPRs), i.e., regression models including deterministic variables and integrated processes, as well as integer powers, of integrated processes as regressors. The regressors are allowed to be endogenous, and the stationary errors are allowed [...] Read more.
This paper develops residual-based monitoring procedures for cointegrating polynomial regressions (CPRs), i.e., regression models including deterministic variables and integrated processes, as well as integer powers, of integrated processes as regressors. The regressors are allowed to be endogenous, and the stationary errors are allowed to be serially correlated. We consider five variants of monitoring statistics and develop the results for three modified least squares estimators for the parameters of the CPRs. The simulations show that using the combination of self-normalization and a moving window leads to the best performance. We use the developed monitoring statistics to assess the structural stability of environmental Kuznets curves (EKCs) for both CO2 and SO2 emissions for twelve industrialized countries since the first oil price shock. Full article
(This article belongs to the Special Issue Econometric Analysis of Climate Change)
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Open AccessArticle
Temperature Anomalies, Long Memory, and Aggregation
Econometrics 2021, 9(1), 9; https://doi.org/10.3390/econometrics9010009 - 03 Mar 2021
Viewed by 369
Abstract
Econometric studies for global heating have typically used regional or global temperature averages to study its long memory properties. One typical explanation behind the long memory properties of temperature averages is cross-sectional aggregation. Nonetheless, formal analysis regarding the effect that aggregation has on [...] Read more.
Econometric studies for global heating have typically used regional or global temperature averages to study its long memory properties. One typical explanation behind the long memory properties of temperature averages is cross-sectional aggregation. Nonetheless, formal analysis regarding the effect that aggregation has on the long memory dynamics of temperature data has been missing. Thus, this paper studies the long memory properties of individual grid temperatures and compares them against the long memory dynamics of global and regional averages. Our results show that the long memory parameters in individual grid observations are smaller than those from regional averages. Global and regional long memory estimates are greatly affected by temperature measurements at the Tropics, where the data is less reliable. Thus, this paper supports the notion that aggregation may be exacerbating the long memory estimated in regional and global temperature data. The results are robust to the bandwidth parameter, limit for station radius of influence, and sampling frequency. Full article
(This article belongs to the Special Issue Econometric Analysis of Climate Change)
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Open AccessArticle
Nonlinear Cointegrating Regression of the Earth’s Surface Mean Temperature Anomalies on Total Radiative Forcing
Econometrics 2021, 9(1), 6; https://doi.org/10.3390/econometrics9010006 - 08 Feb 2021
Viewed by 494
Abstract
This study proposes a nonlinear cointegrating regression model based on the well-known energy balance climate model. Specifically, I investigate the nonlinear cointegrating regression of the mean of temperature anomaly distributions on total radiative forcing using estimated spatial distributions of temperature anomalies for the [...] Read more.
This study proposes a nonlinear cointegrating regression model based on the well-known energy balance climate model. Specifically, I investigate the nonlinear cointegrating regression of the mean of temperature anomaly distributions on total radiative forcing using estimated spatial distributions of temperature anomalies for the Globe, Northern Hemisphere, and Southern Hemisphere. Further, I provide two types of nonlinear response functions that map the total radiative forcing level to mean temperature anomalies. The proposed statistical model provides a climatological implication that spatially heterogenous warming effects play a significant role in identifying nonlinear climate sensitivity. Cointegration and specification tests are provided that support the existence of nonlinear effects of total radiative forcing. Full article
(This article belongs to the Special Issue Econometric Analysis of Climate Change)
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Open AccessFeature PaperArticle
Climate Disaster Risks—Empirics and a Multi-Phase Dynamic Model
Econometrics 2020, 8(3), 33; https://doi.org/10.3390/econometrics8030033 - 18 Aug 2020
Cited by 1 | Viewed by 1319
Abstract
Recent research in financial economics has shown that rare large disasters have the potential to disrupt financial sectors via the destruction of capital stocks and jumps in risk premia. These disruptions often entail negative feedback effects on the macroeconomy. Research on disaster risks [...] Read more.
Recent research in financial economics has shown that rare large disasters have the potential to disrupt financial sectors via the destruction of capital stocks and jumps in risk premia. These disruptions often entail negative feedback effects on the macroeconomy. Research on disaster risks has also actively been pursued in the macroeconomic models of climate change. Our paper uses insights from the former work to study disaster risks in the macroeconomics of climate change and to spell out policy needs. Empirically, the link between carbon dioxide emission and the frequency of climate related disaster is investigated using a panel data approach. The modeling part then uses a multi-phase dynamic macro model to explore the effects of rare large disasters resulting in capital losses and rising risk premia. Our proposed multi-phase dynamic model, incorporating climate-related disaster shocks and their aftermath as a distressed phase, is suitable for studying mitigation and adaptation policies as well as recovery policies. Full article
(This article belongs to the Special Issue Econometric Analysis of Climate Change)
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Open AccessFeature PaperArticle
Dynamic Panel Modeling of Climate Change
Econometrics 2020, 8(3), 30; https://doi.org/10.3390/econometrics8030030 - 28 Jul 2020
Viewed by 1486
Abstract
We discuss some conceptual and practical issues that arise from the presence of global energy balance effects on station level adjustment mechanisms in dynamic panel regressions with climate data. The paper provides asymptotic analyses, observational data computations, and Monte Carlo simulations to assess [...] Read more.
We discuss some conceptual and practical issues that arise from the presence of global energy balance effects on station level adjustment mechanisms in dynamic panel regressions with climate data. The paper provides asymptotic analyses, observational data computations, and Monte Carlo simulations to assess the use of various estimation methodologies, including standard dynamic panel regression and cointegration techniques that have been used in earlier research. The findings reveal massive bias in system GMM estimation of the dynamic panel regression parameters, which arise from fixed effect heterogeneity across individual station level observations. Difference GMM and Within Group (WG) estimation have little bias and WG estimation is recommended for practical implementation of dynamic panel regression with highly disaggregated climate data. Intriguingly, from an econometric perspective and importantly for global policy analysis, it is shown that in this model despite the substantial differences between the estimates of the regression model parameters, estimates of global transient climate sensitivity (of temperature to a doubling of atmospheric CO2) are robust to the estimation method employed and to the specific nature of the trending mechanism in global temperature, radiation, and CO2. Full article
(This article belongs to the Special Issue Econometric Analysis of Climate Change)
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Open AccessFeature PaperArticle
Frequency-Domain Evidence for Climate Change
Econometrics 2020, 8(3), 28; https://doi.org/10.3390/econometrics8030028 - 20 Jul 2020
Cited by 1 | Viewed by 1304
Abstract
The goal of this paper is to search for conclusive evidence against the stationarity of the global air surface temperature, which is one of the most important indicators of climate change. For this purpose, possible long-range dependencies are investigated in the frequency-domain. Since [...] Read more.
The goal of this paper is to search for conclusive evidence against the stationarity of the global air surface temperature, which is one of the most important indicators of climate change. For this purpose, possible long-range dependencies are investigated in the frequency-domain. Since conventional tests of hypotheses about the memory parameter, which measures the degree of long-range dependence, are typically based on asymptotic arguments and are therefore of limited practical value in case of small or medium sample sizes, we employ a new small-sample test as well as a related estimator for the memory parameter. To safeguard against false positive findings, simulation studies are carried out to examine the suitability of the employed methods and hemispheric datasets are used to check the robustness of the empirical findings against low-frequency natural variability caused by oceanic cycles. Overall, our frequency-domain analysis provides strong evidence of non-stationarity, which is consistent with previous results obtained in the time domain with models allowing for stochastic or deterministic trends. Full article
(This article belongs to the Special Issue Econometric Analysis of Climate Change)
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Open AccessArticle
Tornado Occurrences in the United States: A Spatio-Temporal Point Process Approach
Econometrics 2020, 8(2), 25; https://doi.org/10.3390/econometrics8020025 - 11 Jun 2020
Viewed by 1654
Abstract
In this paper, we analyze the tornado occurrences in the Unites States. To perform inference procedures for the spatio-temporal point process we adopt a dynamic representation of Log-Gaussian Cox Process. This representation is based on the decomposition of intensity function in components of [...] Read more.
In this paper, we analyze the tornado occurrences in the Unites States. To perform inference procedures for the spatio-temporal point process we adopt a dynamic representation of Log-Gaussian Cox Process. This representation is based on the decomposition of intensity function in components of trend, cycles, and spatial effects. In this model, spatial effects are also represented by a dynamic functional structure, which allows analyzing the possible changes in the spatio-temporal distribution of the occurrence of tornadoes due to possible changes in climate patterns. The model was estimated using Bayesian inference through the Integrated Nested Laplace Approximations. We use data from the Storm Prediction Center’s Severe Weather Database between 1954 and 2018, and the results provided evidence, from new perspectives, that trends in annual tornado occurrences in the United States have remained relatively constant, supporting previously reported findings. Full article
(This article belongs to the Special Issue Econometric Analysis of Climate Change)
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Open AccessEditor’s ChoiceArticle
Forecast Accuracy Matters for Hurricane Damage
Econometrics 2020, 8(2), 18; https://doi.org/10.3390/econometrics8020018 - 14 May 2020
Cited by 1 | Viewed by 2412
Abstract
I analyze damage from hurricane strikes on the United States since 1955. Using machine learning methods to select the most important drivers for damage, I show that large errors in a hurricane’s predicted landfall location result in higher damage. This relationship holds across [...] Read more.
I analyze damage from hurricane strikes on the United States since 1955. Using machine learning methods to select the most important drivers for damage, I show that large errors in a hurricane’s predicted landfall location result in higher damage. This relationship holds across a wide range of model specifications and when controlling for ex-ante uncertainty and potential endogeneity. Using a counterfactual exercise I find that the cumulative reduction in damage from forecast improvements since 1970 is about $82 billion, which exceeds the U.S. government’s spending on the forecasts and private willingness to pay for them. Full article
(This article belongs to the Special Issue Econometric Analysis of Climate Change)
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