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Modeling Macro-Political Dynamics

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  • Brandt, Patrick T.
  • Freeman, John R.

Abstract

Analyzing macro-political processes is complicated by four interrelated problems: model scale, endogeneity, persistence, and specification uncertainty. These problems are endemic in the study of political economy, public opinion, international relations, and other kinds of macro-political research. We show how a Bayesian structural time series approach addresses them. Our illustration is a structurally identified, nine-equation model of the U.S. political-economic system. It combines key features of the model of Erikson, MacKuen, and Stimson (2002) of the American macropolity with those of a leading macroeconomic model of the United States (Sims and Zha, 1998; Leeper, Sims, and Zha, 1996). This Bayesian structural model, with a loosely informed prior, yields the best performance in terms of model fit and dynamics. This model 1) confirms existing results about the countercyclical nature of monetary policy (Williams 1990); 2) reveals informational sources of approval dynamics: innovations in information variables affect consumer sentiment and approval and the impacts on consumer sentiment feed-forward into subsequent approval changes; 3) finds that the real economy does not have any major impacts on key macropolity variables; and 4) concludes, contrary to Erikson, MacKuen, and Stimson (2002), that macropartisanship does not depend on the evolution of the real economy in the short or medium term and only very weakly on informational variables in the long term.

Suggested Citation

  • Brandt, Patrick T. & Freeman, John R., 2009. "Modeling Macro-Political Dynamics," Political Analysis, Cambridge University Press, vol. 17(2), pages 113-142, April.
  • Handle: RePEc:cup:polals:v:17:y:2009:i:02:p:113-142_00
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    Cited by:

    1. Campos, Luciano & Casas, Agustín, 2021. "Rara Avis: Latin American populism in the 21st century," European Journal of Political Economy, Elsevier, vol. 70(C).
    2. Michael M. Bechtel & Dirk Leuffen, 2010. "Forecasting European Union politics: Real-time forecasts in political time series analysis," European Union Politics, , vol. 11(2), pages 309-327, June.
    3. Lopez-Buenache, German, 2019. "The evolution of monetary policy effectiveness under macroeconomic instability," Economic Modelling, Elsevier, vol. 83(C), pages 221-233.
    4. Patrick T. Brandt & John R. Freeman & Philip A. Schrodt, 2011. "Real Time, Time Series Forecasting of Inter- and Intra-State Political Conflict," Conflict Management and Peace Science, Peace Science Society (International), vol. 28(1), pages 41-64, February.
    5. Dimiter Toshkov, 2011. "Public opinion and policy output in the European Union: A lost relationship," European Union Politics, , vol. 12(2), pages 169-191, June.
    6. Butkiewicz, James L. & Solcan, Mihaela, 2016. "The original Operation Twist: the War Finance Corporation's war bond purchases, 1918–1920," Financial History Review, Cambridge University Press, vol. 23(1), pages 21-46, April.
    7. Gazi M. Hassan & Shafiqur Rahman, 2015. "Is the Democratisation Process Responsive to Remittance Flows? Evidence from Bangladesh," Working Papers in Economics 15/06, University of Waikato.
    8. Davis, Brent, 2017. "“Taking Occam’s Razor to the Endogeneity Problem in Economic Voting”," MPRA Paper 80732, University Library of Munich, Germany.
    9. Thomas Zeitzoff, 2018. "Does Social Media Influence Conflict? Evidence from the 2012 Gaza Conflict," Journal of Conflict Resolution, Peace Science Society (International), vol. 62(1), pages 29-63, January.
    10. Brandt, Patrick T. & Freeman, John R. & Schrodt, Philip A., 2014. "Evaluating forecasts of political conflict dynamics," International Journal of Forecasting, Elsevier, vol. 30(4), pages 944-962.
    11. Myriam Gómez-Méndez & Erwin Hansen, 2021. "Economic policy uncertainty and presidential approval: Evidence from Latin America," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-17, March.
    12. Hanck, Christoph & Prüser, Jan, 2016. "House prices and interest rates: Bayesian evidence from Germany," Ruhr Economic Papers 620, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    13. Prüser Jan & Hanck Christoph, 2021. "A Comparison of Approaches to Select the Informativeness of Priors in BVARs," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 241(4), pages 501-525, August.

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