IDEAS home Printed from https://ideas.repec.org/p/ces/ceswps/_8615.html
   My bibliography  Save this paper

Regional Heterogeneity and U.S. Presidential Elections

Author

Listed:
  • Rashad Ahmed
  • M. Hashem Pesaran

Abstract

This paper develops a recursive model of voter turnout and voting outcomes at U.S. county level to investigate the socioeconomic determinants of recent U.S. presidential elections. It is shown that the relationship between many socioeconomic variables and voting outcomes is not uniform across U.S. regions. By allowing for regional heterogeneity and using high-dimensional variable selection algorithms, we can explain and correctly predict the unexpected 2016 Republican victory. Key factors explaining voting outcomes include incumbency effects, voter turnout, local economic performance, unemployment, poverty, educational attainment, house price changes, urban-rural scores, and international competitiveness. Our results corroborate evidence of ‘short-memory’ among voters: economic fluctuations realized a few months prior to the election are indeed powerful predictors of voting outcomes as compared to their longer- term analogues. The paper then presents real time forecasts for the 2020 U.S. Presidential Election based on data available at the end of July 2020 which are then updated based on data available as of mid-October.

Suggested Citation

  • Rashad Ahmed & M. Hashem Pesaran, 2020. "Regional Heterogeneity and U.S. Presidential Elections," CESifo Working Paper Series 8615, CESifo.
    • Handle: RePEc:ces:ceswps:_8615
    as

    Download full text from publisher

    File URL: https://www.cesifo.org/DocDL/cesifo1_wp8615.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. A. Chudik & G. Kapetanios & M. Hashem Pesaran, 2018. "A One Covariate at a Time, Multiple Testing Approach to Variable Selection in High‐Dimensional Linear Regression Models," Econometrica, Econometric Society, vol. 86(4), pages 1479-1512, July.
    2. Alexander Chudik & M. Hashem Pesaran & Mahrad Sharifvaghefi, 2020. "Variable Selection and Forecasting in High Dimensional Linear Regressions with Structural Breaks," CESifo Working Paper Series 8475, CESifo.
    3. Pesaran, M. Hashem, 2015. "Time Series and Panel Data Econometrics," OUP Catalogue, Oxford University Press, number 9780198759980.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Boumans, Dorine & Gründler, Klaus & Potrafke, Niklas & Ruthardt, Fabian, 2024. "Political leaders and macroeconomic expectations: Evidence from a global survey experiment," Journal of Public Economics, Elsevier, vol. 235(C).

    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. Pesaran, M. Hashem & Yang, Cynthia Fan, 2020. "Econometric analysis of production networks with dominant units," Journal of Econometrics, Elsevier, vol. 219(2), pages 507-541.
    2. Natalia Bailey & George Kapetanios & M. Hashem Pesaran, 2021. "Measurement of factor strength: Theory and practice," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(5), pages 587-613, August.
    3. Claudia García-García & Catalina B. García-García & Román Salmerón, 2021. "Confronting collinearity in environmental regression models: evidence from world data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(3), pages 895-926, September.
    4. Viet Hoang Dinh & Didier Nibbering & Benjamin Wong, 2023. "Random Subspace Local Projections," CAMA Working Papers 2023-34, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    5. Ghosh, Soumya Kanti & Nath, Hiranya K., 2023. "What determines private and household savings in India?," International Review of Economics & Finance, Elsevier, vol. 86(C), pages 639-651.
    6. Chudik, Alexander & Pesaran, M. Hashem, 2019. "Mean group estimation in presence of weakly cross-correlated estimators," Economics Letters, Elsevier, vol. 175(C), pages 101-105.
    7. Francisco Javier Forcadell & Fernando Úbeda, 2022. "Individual entrepreneurial orientation and performance: the mediating role of international entrepreneurship," International Entrepreneurship and Management Journal, Springer, vol. 18(2), pages 875-900, June.
    8. Emmanuel Anyigbah & Yusheng Kong & Bless Kofi Edziah & Ahotovi Thomas Ahoto & Wilhelmina Seyome Ahiaku, 2023. "Board Characteristics and Corporate Sustainability Reporting: Evidence from Chinese Listed Companies," Sustainability, MDPI, vol. 15(4), pages 1-26, February.
    9. Bowei Guo & Giorgio Castagneto Gissey, 2019. "Cost Pass-through in the British Wholesale Electricity Market: Implications of Brexit and the ETS reform," Working Papers EPRG1937, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    10. Polemis, Michael & Tselekounis, Markos, 2019. "Does deregulation drive innovation intensity? Lessons learned from the OECD telecommunications sector," MPRA Paper 92770, University Library of Munich, Germany.
    11. Arestis, Philip & Ferreiro, Jesus & Gomez, Carmen, 2023. "Does employment protection legislation affect employment and unemployment?11We acknowledge the comments of an editor and an associate editor of the journal and three reviewers. Their suggestions and r," Economic Modelling, Elsevier, vol. 126(C).
    12. Elisa Cavatorta & Ron P. Smith, 2017. "Factor Models in Panels with Cross-sectional Dependence: An Application to the Extended SIPRI Military Expenditure Data," Defence and Peace Economics, Taylor & Francis Journals, vol. 28(4), pages 437-456, July.
    13. Catalán, Mario & Hoffmaister, Alexander W., 2022. "When banks punch back: Macrofinancial feedback loops in stress tests," Journal of International Money and Finance, Elsevier, vol. 124(C).
    14. Kristin Forbes & Ida Hjortsoe & Tsvetelina Nenova, 2020. "International Evidence on Shock-Dependent Exchange Rate Pass-Through," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 68(4), pages 721-763, December.
    15. Raslan Alzubi & Mustafa Caglayan & Kostas Mouratidis, 2017. "The Risk-Taking Channel in the US: A GVAR Approach," Working Papers 2017009, The University of Sheffield, Department of Economics.
    16. Thomas H. W. Ziesemer, 2022. "Foreign R&D spillovers to the USA and strategic reactions," Applied Economics, Taylor & Francis Journals, vol. 54(37), pages 4274-4291, August.
    17. Bryan T. Kelly & Asaf Manela & Alan Moreira, 2019. "Text Selection," NBER Working Papers 26517, National Bureau of Economic Research, Inc.
    18. Jad Beyhum & Eric Gautier, 2020. "Factor and factor loading augmented estimators for panel regression," Working Papers hal-02957008, HAL.
    19. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2021. "Economic Predictions With Big Data: The Illusion of Sparsity," Econometrica, Econometric Society, vol. 89(5), pages 2409-2437, September.
    20. Ahmad, Khurshid & Han, JingGuang & Hutson, Elaine & Kearney, Colm & Liu, Sha, 2016. "Media-expressed negative tone and firm-level stock returns," Journal of Corporate Finance, Elsevier, vol. 37(C), pages 152-172.

    More about this item

    Keywords

    voter turnout; popular and electoral college votes; simultaneity and recursive identification; high dimensional forecasting models; Lasso; OCMT;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • D72 - Microeconomics - - Analysis of Collective Decision-Making - - - Political Processes: Rent-seeking, Lobbying, Elections, Legislatures, and Voting Behavior

    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:ces:ceswps:_8615. 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: Klaus Wohlrabe (email available below). General contact details of provider: https://edirc.repec.org/data/cesifde.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.