IDEAS home Printed from https://ideas.repec.org/p/pre/wpaper/202251.html
   My bibliography  Save this paper

Climate Risks and Forecastability of the Weekly State-Level Economic Conditions of the United States

Author

Listed:
  • Oguzhan Cepni

    (Copenhagen Business School, Department of Economics, Porcelaenshaven 16A, Frederiksberg DK-2000, Denmark)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)

  • Wenting Liao

    (School of Finance, Renmin University of China, Beijing, People's Republic of China)

  • Jun Ma

    (Department of Economics, Northeastern University, 301 Lake Hall, Boston, Massachusetts, 02115, United States)

Abstract

In this paper, we first utilize a Dynamic Factor Model with Stochastic Volatility (DFM-SV) to filter out the national factor from the local components of weekly state-level economic conditions indexes of the United States (US) over the period of April 1987 to August 2021. In the second step, we forecast the state-level factors in a panel data set-up based on the information content of corresponding state-level climate risks, as proxied by changes in temperature and its SV. The forecasting experiment depicts statistically significant evidence of out-of-sample predictability over a one-month- to one-year-ahead horizon, with stronger forecasting gains derived for states that do not believe that climate change is happening and are Republican. We also find evidence of national climate risks in accurately forecasting the national factor of economic conditions. Our analyses have important policy implications from a regional perspective.

Suggested Citation

  • Oguzhan Cepni & Rangan Gupta & Wenting Liao & Jun Ma, 2022. "Climate Risks and Forecastability of the Weekly State-Level Economic Conditions of the United States," Working Papers 202251, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202251
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Riccardo Colacito & Bridget Hoffmann & Toan Phan, 2019. "Temperature and Growth: A Panel Analysis of the United States," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 51(2-3), pages 313-368, March.
    2. Sheng, Xin & Gupta, Rangan & Çepni, Oğuzhan, 2022. "The effects of climate risks on economic activity in a panel of US states: The role of uncertainty," Economics Letters, Elsevier, vol. 213(C).
    3. John Y. Campbell, 2008. "Viewpoint: Estimating the equity premium," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 41(1), pages 1-21, February.
    4. Michael Donadelli & Marcus Jüppner & Antonio Paradiso & Christian Schlag, 2021. "Computing Macro-Effects and Welfare Costs of Temperature Volatility: A Structural Approach," Computational Economics, Springer;Society for Computational Economics, vol. 58(2), pages 347-394, August.
    5. Donadelli, M. & Jüppner, M. & Riedel, M. & Schlag, C., 2017. "Temperature shocks and welfare costs," Journal of Economic Dynamics and Control, Elsevier, vol. 82(C), pages 331-355.
    6. Gupta, Rangan & Ma, Jun & Risse, Marian & Wohar, Mark E., 2018. "Common business cycles and volatilities in US states and MSAs: The role of economic uncertainty," Journal of Macroeconomics, Elsevier, vol. 57(C), pages 317-337.
    7. Michael Donadelli & Marcus Jüppner & Sergio Vergalli, 2022. "Temperature Variability and the Macroeconomy: A World Tour," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 83(1), pages 221-259, September.
    8. John Y. Campbell, 2007. "Estimating the Equity Premium," NBER Working Papers 13423, National Bureau of Economic Research, Inc.
    9. Marco Del Negro & Christopher Otrok, 2008. "Dynamic factor models with time-varying parameters: measuring changes in international business cycles," Staff Reports 326, Federal Reserve Bank of New York.
    10. Markus Baldauf & Lorenzo Garlappi & Constantine Yannelis & José Scheinkman, 2020. "Does Climate Change Affect Real Estate Prices? Only If You Believe In It," The Review of Financial Studies, Society for Financial Studies, vol. 33(3), pages 1256-1295.
    11. Sheng, Xin & Gupta, Rangan & Cepni, Oguzhan, 2022. "Persistence of state-level uncertainty of the United States: The role of climate risks," Economics Letters, Elsevier, vol. 215(C).
    12. Haroon Mumtaz & Laura Sunder‐Plassmann & Angeliki Theophilopoulou, 2018. "The State‐Level Impact of Uncertainty Shocks," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 50(8), pages 1879-1899, December.
    13. Furkan Emirmahmutoglu & Mehmet Bacilar & Nicholas Apergis & Beatrice D. Simo-Kengne & Tsangyao Chang & Rangan Gupta, 2016. "Causal Relationship between Asset Prices and Output in the United States: Evidence from the State-Level Panel Granger Causality Test," Regional Studies, Taylor & Francis Journals, vol. 50(10), pages 1728-1741, October.
    14. McCracken, Michael W., 2007. "Asymptotics for out of sample tests of Granger causality," Journal of Econometrics, Elsevier, vol. 140(2), pages 719-752, October.
    15. Laura A Bakkensen & Lint Barrage, 2022. "Going Underwater? Flood Risk Belief Heterogeneity and Coastal Home Price Dynamics," The Review of Financial Studies, Society for Financial Studies, vol. 35(8), pages 3666-3709.
    16. Mumtaz, Haroon, 2018. "Does uncertainty affect real activity? Evidence from state-level data," Economics Letters, Elsevier, vol. 167(C), pages 127-130.
    17. Bhatt, Vipul & Kishor, N Kundan & Ma, Jun, 2017. "The impact of EMU on bond yield convergence: Evidence from a time-varying dynamic factor model," Journal of Economic Dynamics and Control, Elsevier, vol. 82(C), pages 206-222.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cepni, Oguzhan & Christou, Christina & Gupta, Rangan, 2023. "Forecasting national recessions of the United States with state-level climate risks: Evidence from model averaging in Markov-switching models," Economics Letters, Elsevier, vol. 227(C).
    2. Xin Sheng & Rangan Gupta & Wenting Liao & Oguzhan Cepni, 2024. "The Effects of Uncertainty on Economic Conditions across US States: The Role of Climate Risks," Working Papers 202410, University of Pretoria, Department of Economics.
    3. Sheng, Xin & Gupta, Rangan & Cepni, Oguzhan, 2022. "Persistence of state-level uncertainty of the United States: The role of climate risks," Economics Letters, Elsevier, vol. 215(C).
    4. Mohammad Reza Yeganegi & Hossein Hassani & Rangan Gupta, 2023. "The ENSO cycle and forecastability of global inflation and output growth: Evidence from standard and mixed‐frequency multivariate singular spectrum analyses," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1690-1707, November.
    5. Massimiliano Caporin & Petre Caraiani & Oguzhan Cepni & Rangan Gupta, 2024. "Predicting the Conditional Distribution of US Stock Market Systemic Stress: The Role of Climate Risks," Working Papers 202407, University of Pretoria, Department of Economics.
    6. Mehmet Balcilar & David Gabauer & Rangan Gupta & Christian Pierdzioch, 2021. "Uncertainty and Forecastability of Regional Output Growth in the United Kingdom: Evidence from Machine Learning," Working Papers 202111, University of Pretoria, Department of Economics.
    7. Demirer, Riza & Gupta, Rangan & Salisu, Afees A. & van Eyden, Reneé, 2023. "Firm-level business uncertainty and the predictability of the aggregate U.S. stock market volatility during the COVID-19 pandemic," The Quarterly Review of Economics and Finance, Elsevier, vol. 88(C), pages 295-302.
    8. Liao, Wenting & Sheng, Xin & Gupta, Rangan & Karmakar, Sayar, 2024. "Extreme weather shocks and state-level inflation of the United States," Economics Letters, Elsevier, vol. 238(C).
    9. Rangan Gupta & Hardik A. Marfatia & Christian Pierdzioch & Afees A. Salisu, 2022. "Machine Learning Predictions of Housing Market Synchronization across US States: The Role of Uncertainty," The Journal of Real Estate Finance and Economics, Springer, vol. 64(4), pages 523-545, May.
    10. Reneé van Eyden & Rangan Gupta & Christophe André & Xin Sheng, 2022. "The effect of macroeconomic uncertainty on housing returns and volatility: evidence from US state-level data," Chapters, in: Charles K.Y. Leung (ed.), Handbook of Real Estate and Macroeconomics, chapter 8, pages 206-238, Edward Elgar Publishing.
    11. Rangan Gupta & Anandamayee Majumdar & Christian Pierdzioch & Onur Polat, 2024. "Climate Risks and Real Gold Returns over 750 Years," Forecasting, MDPI, vol. 6(4), pages 1-16, October.
    12. Mehmet Balcilar & David Gabauer & Rangan Gupta & Christian Pierdzioch, 2022. "Uncertainty and forecastability of regional output growth in the UK: Evidence from machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1049-1064, September.
    13. Afees A. Salisu & Christian Pierdzioch & Rangan Gupta & Reneé van Eyden, 2023. "Climate risks and U.S. stock‐market tail risks: A forecasting experiment using over a century of data," International Review of Finance, International Review of Finance Ltd., vol. 23(2), pages 228-244, June.
    14. Kejin Wu & Sayar Karmakar & Rangan Gupta & Christian Pierdzioch, 2023. "Climate Risks and Stock Market Volatility Over a Century in an Emerging Market Economy: The Case of South Africa," Working Papers 202326, University of Pretoria, Department of Economics.
    15. Elie Bouri & Rangan Gupta & Asingamaanda Liphadzi & Christian Pierdzioch, 2024. "Forecasting Stock Returns Volatility of the G7 Over Centuries: The Role of Climate Risks," Working Papers 202424, University of Pretoria, Department of Economics.
    16. Pierdzioch Christian & Gupta Rangan, 2020. "Uncertainty and Forecasts of U.S. Recessions," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(4), pages 1-20, September.
    17. Gupta, Rangan & Nel, Jacobus & Salisu, Afees A. & Ji, Qiang, 2023. "Predictability of economic slowdowns in advanced countries over eight centuries: The role of climate risks," Finance Research Letters, Elsevier, vol. 54(C).
    18. Gupta, Rangan & Wohar, Mark, 2017. "Forecasting oil and stock returns with a Qual VAR using over 150years off data," Energy Economics, Elsevier, vol. 62(C), pages 181-186.
    19. Mehmet Balcilar & Rangan Gupta & Christian Pierdzioch, 2022. "Oil-Price Uncertainty and International Stock Returns: Dissecting Quantile-Based Predictability and Spillover Effects Using More than a Century of Data," Energies, MDPI, vol. 15(22), pages 1-26, November.
    20. Christina Christou & Rangan Gupta & Christis Hassapis & Tahir Suleman, 2018. "The role of economic uncertainty in forecasting exchange rate returns and realized volatility: Evidence from quantile predictive regressions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(7), pages 705-719, November.

    More about this item

    Keywords

    State-Level Economic Conditions; Climate Risks; Dynamic Factor Model with Stochastic Volatility; Panel Predictive Regression; Forecasting;
    All these keywords.

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E66 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - General Outlook and Conditions
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pre:wpaper:202251. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Rangan Gupta (email available below). General contact details of provider: https://edirc.repec.org/data/decupza.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.