Skip to main content Accessibility help
×
Home

Modeling Macro-Political Dynamics

  • Patrick T. Brandt (a1) and John R. Freeman (a2)

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.

Copyright

Corresponding author

e-mail: pbrandt@utdallas.edu (corresponding author)

Footnotes

Hide All

Authors' note: A previous version of this paper was presented at the 2005 Annual Meeting of the American Political Science Association, Washington, DC, and at a seminar at the College of William and Mary. The authors thank Janet Box-Steffensmeier, Harold Clarke, Brian Collins, Chetan Dave, Larry Evans, Jeff Gill, Simon Jackman, and Ron Rappoport for useful feedback and comments. Replication materials, additional appendices, and additional results are available on the Political Analysis Web site or from P.T.B. The authors are solely responsible for the contents.

Footnotes

References

Hide All
Alesina, Alberto, and Rosenthal, Howard. 1995. Partisan politics, divided government and the economy. Cambridge: Cambridge University Press.
Alesina, Alberto, Londregan, John, and Rosenthal, Howard. 1993. A model of the political economy of the United States. American Political Science Review 87: 1233.
Bartels, Larry M. 1991. Instrumental and “quasi-instrumental” variables. American Journal of Political Science 35(3): 777800.
Bartlett, M. S. 1957. A comment on D. V. Lindley's statistical paradox. Biometrika 44 (3/4): 533534.
Beck, Nathaniel. 1987. Elections and the fed: Is there a political monetary cycle? American Journal of Political Science 31(1): 194216.
Bernanke, B. 1986. Alternative explanations of the money-income correlation. Carnegie-Rochester Conference Series on Public Policy 25(1): 4999.
Bernardo, Jose M. 1979. Reference posterior distributions for Bayesian inference. Journal of the Royal Statistical Society. Series B (Methodological) 41(2): 113–47.
Bernhard, William, and Leblang, David. 2006. Democratic processes and financial markets: Pricing politics. New York: Cambridge University Press.
Blanchard, O., and Quah, D. 1989. The dynamic effects of aggregate demand and supply disturbances. American Economic Review 79: 655–73.
Box-Steffensmeier, Janet M., and Smith, Renee M. 1996. The dynamics of aggregate partisanship. American Political Science Review 90(3): 567–80.
Brandt, Patrick T., and Freeman, John R. 2006. Advances in Bayesian time series modeling and the study of politics: Theory testing, forecasting, and policy analysis. Political Analysis 14(1): 136.
Brandt, Patrick T., and Williams, John T. 2007. Multiple time series models. Beverly Hills, CA: Sage.
Brandt, Patrick T., Colaresi, Michael, and Freeman, John R. 2008. The dynamics of reciprocity, accountability, and credibility. Journal of Conflict Resolution 52: 343–74.
Carlin, Bradley P., and Louis, Thomas A. 2000. Bayes and empirical Bayes methods for data analysis. 2nd ed. Boca Raton, FL: Chapman & Hall/CRC.
Chib, Siddartha. 1995. Marginal likelihood from the Gibbs output. Journal of the American Statistical Association 90(432): 1313–21.
Clarke, Harol D., Ho, Karl, and Stewart, Marianne C. 2000. Major's lesser (not minor) effects: Prime ministerial approval and governing party support in Britain since 1979. Electoral Studies 19 (2–3): 255–73.
Clarke, Harol D., and Stewart, Marianne C. 1995. Economic evaluations, prime ministerial approval and governing party support: Rival models reconsidered. British Journal of Political Science 25(2): 145–70.
Cortes, Fernando, Przeworski, Adam, and Sprague, John. 1974. System analysis for social scientists. New York: John Wiley and Sons.
Cushman, David O., and Zha, Tao. 1997. Identifying monetary policy in a small open economy under flexible exchange rates. Journal of Monetary Economics 39: 433–48.
De Boef, Suzanna, and Granato, James. 1997. Near integrated data and the analysis of political relationships. American Journal of Political Science 41(2): 619–40.
De Boef, Suzanna L., and Luke, Keele. 2008. Taking time seriously. American Journal of Political Science 52: 184200.
Del Negro, Marco, and Schorfheide, Frank. 2004. Priors from general equilibrium models for VARs. International Economic Review 45: 643–73.
Doan, Thomas, Litterman, Robert, and Sims, Christopher. 1984. Forecasting and conditional projection using realistic prior distributions. Econometric Reviews 3: 1100.
Edwards, George C., and Dan Wood, B. 1999. Who influences whom? The president and the public agenda. American Political Science Review 93(2): 327–44.
Erikson, Robert S., MacKuen, Michael B., and Stimson, James A. 1998. What moves macropartisanship? A response to Green, Palmquist and Schickler. American Political Science Review 92(4): 901–12.
Erikson, Robert S., MacKuen, Michael B., and Stimson, James A. 2002. The macropolity. New York: Cambridge University Press.
Franzese, Robert F. 2002. Macroeconomic policies of developed democracies, Cambridge studies in comparative politics. New York: Cambridge University Press.
Franzese, Robert F., and Hays, Jude. 2005. Empirical modeling strategies for spatial interdependence: Omitted variable vs. simultaneity bias. Paper originally presented at the Annual Meeting of the Political Methodology Society, Stanford University, Palo Alto, CA.
Freeman, John R. 2005. Modeling macropolitics: EITM and Reality. Paper presented at the EITM Workshop, Canadian Political Science Association Meetings. London: Ontario.
Freeman, John R., Williams, John T., Houser, Daniel, and Kellstedt, Paul. 1998. Long memoried processes, unit roots and causal inference in political science. American Journal of Political Science 42(4): 1289–327.
Freeman, John R., Williams, John T., and Lin, Tse-Min. 1989. Vector autoregression and the study of politics. American Journal of Political Science 33: 842–77.
Garthwaite, Paul H., Kadane, Joseph B., and O'Hagan, Anthony. 2005. Statistical methods for eliciting probability distributions. Journal of the American Statistical Association 100(470): 680700.
Geweke, John. 2005. Contemporary Bayesian econometrics and statistics. Hoboken, NJ: John Wiley & Sons.
Gill, Jeff. 2004. Introducing the special issue of political analysis on Bayesian methods. Political Analysis 12(4): 323–37.
Gill, Jeff. 2007. Bayesian methods: A social and behavioral sciences approach. 2nd edn. Boca Raton, FL: Chapman and Hall.
Goldstein, Joshua, and Pevehouse, Jon C. 1997. Reciprocity, bullying and international cooperation: Time series analysis of the Bosnian conflict. American Political Science Review 91: 515–24.
Goldstein, Joshua S., Pevehouse, Jon C., Gerner, Deborah J., and Telhami, Shibley. 2001. Reciprocity, triangularity, and cooperation in the Middle East, 1979–1997. Journal of Conflict Resolution 45(5): 594620.
Green, Donald, Palmquist, Bradley, and Schickler, Eric. 1998. Macropartisanship: A replication and critique. American Political Science Review 92(4): 883900.
Hamilton, James D. 1994. Time series analysis. Princeton, NJ: Princeton University Press.
Ingram, Beth F., and Whiteman, Charles H. 1994. Supplanting the ‘Minnesota’ prior: Forecasting macroeconomic time series using real business cycle model priors. Journal of Monetary Economics 34: 497510.
Jackman, Simon. 2004. Bayesian analysis in political science. Annual Reviews of Political Science 7: 483505.
Jackman, Simon. 2008. Bayesian analysis for the social sciences. Hoboken, NJ: John Wiley & Sons.
Kadane, Joseph B., Chan, Ngai Hang, and Wolfson, Lara J. 1996. Priors for unit root models. Journal of Econometrics 75: 99111.
Kadiyala, K. Rao, and Karlsson, Sune. 1997. Numerical methods for estimation and inference in Bayesian VAR-model. Journal of Applied Econometrics 12: 99132.
Kass, Robert E., and Raftery, Adrian E. 1995. Bayes factors. Journal of the American Statistical Association 90(430): 773–95.
Leeper, Eric M., Sims, Christopher A., and Zha, Tao A. 1996. What does monetary policy do? Brookings Papers on Economic Activity 1996(2): 163.
Litterman, Robert B. 1980. Techniques for forecasting with vector autoregressions. PhD thesis University of Minnesota.
Litterman, Robert B. 1986. Forecasting with Bayesian vector autoregressions—Five years of experience. Journal of Business, Economics and Statistics 4: 2538.
Manski, Charles. 1995. Identification problems in the social sciences. Cambridge, MA: Harvard University Press.
Martin, Andrew, and Quinn, Kevin. 2002. Dynamic ideal point estimation via Markov chain Monte Carlo for the U.S. Supreme Court. Political Analysis 10(2): 134–53.
Mebane, Walte R. 2000. Coordination, moderation, and institutional balancing in American Presidential and House Elections. American Political Science Review 94: 3753.
Mebane, Walte R. 2003. Congressional campaign contributions, direct service, and electoral outcomes in the United States: Statistical tests of a formal game model with statistical estimates. Political complexity: Nonlinear models of politics, ed. Richards, Diana. Ann Arbor: University of Michigan Press.
Mebane, Walte R. 2005. Partisan messages, unconditional strategies, and coordination in American Elections. Revised version of a paper originally presented at the Annual Meeting of the Political Metholodolgy Society, Stanford University, Palo Alto, CA.
Morris, Irwin L. 2000. Congress, the President, and the Federal Reserve: The politics of American monetary policy-making. Ann Arbor: University of Michigan Press.
Ogata, Ktsuhiko. 1967. State space analysis of control systems. Englewood Cliffs, NJ: Prentice-Hall, Inc.
Ostrom, Charles, and Smith, Renee. 1993. Error correction, attitude persistence, and executive rewards and punishments: A behavioral theory of presidential approval. Political Analysis 3: 127–84.
Pevehouse, Jon C., and Goldstein, Joshua S. 1999. Serbian compliance or defiance in Kosovo? Statistical analysis and real-time predictions. Journal of Conflict Resolution 43(4): 538–46.
Poole, Keith. 1998. Recovering a basic space from a set of issue scales. American Journal of Political Science 42: 954–93.
Poole, Keith T., and Rosenthal, Howard. 1997. Congress: A political-economic history of roll call voting. New York: Oxford University Press.
Richard, Jean Francois 1977. Bayesian analysis of the regression model when the disturbances are generated by an autoregressive process. In New developments in the application of Bayesian methods, eds. Aykac, A. and Brumat, C. Amsterdam: North Holland.
Robertson, John C., and Tallman, Ellis W. 1999. Vector autoregressions: Forecasting and reality. Economic Review (Atlanta Federal Reserve Bank) 84(1): 418.
Robertson, John C., and Tallman, Ellis W. 2001. Improving federal funds rate forecasts in VAR models used for policy analysis. Journal of Business and Economics Statistics 19(3): 324–30.
Sattler, Thomas, Freeman, John R., and Brandt, Patrick T. 2008. Popular sovereignty and the room to maneuver: A search for a causal chain. Comparative Political Studies 41(9): 1212–39.
Sattler, Thomas, Freeman, John R., and Brandt, Patrick T. 2009. Erratum: Popular sovereignty and the room to maneuver: A search for a causal chain. Comparative Political Studies 42(1): 157–63.
Sims, Christopher A. 1972. Money, income, and causality. American Economic Review 62: 540–52.
Sims, Christopher A. 1980. Macroeconomics and reality. Econometrica 48(1): 148.
Sims, Christopher A. 1986a. Are forecasting models usable for policy analysis? Quarterly Review, Federal Reserve Bank of Minneapolis 10: 216.
Sims, Christopher A. 1986b. Specification, estimation, and analysis of macroeconomic models. Journal of Money, Credit and Banking 18(1): 121–6.
Sims, Christopher A. 2005. The state of macroeconomic policy modeling: Where do we go from here? Macroeconomics and reality, 25 years later. Conference, Barcelxd ona, Spain http://www.econ.upf.es/crei/activities/sc_conferences/22/Papers/sims.pdf (accessed April 13, 2005).
Sims, Christopher A., and Zha, Tao A. 1995. Error bands for impulse responses. http://sims.princeton.edu/yftp/ier/ (accessed January 15, 2007).
Sims, Christopher A., and Zha, Tao A. 1998. Bayesian methods for dynamic multivariate models. International Economic Review 39(4): 949–68.
Sims, Christopher A., and Zha, Tao A. 1999. Error bands for impulse responses. Econometrica 67(5): 1113–56.
Theil, Henri. 1963. On the use of incomplete prior information in regression analysis. Journal of the American Statistical Association 58(302): 401–14.
Waggoner, Daniel F., and Zha, Tao A. 2003a. A Gibbs sampler for structural vector autoregressions. Journal of Economic Dynamics & Control 28: 349–66.
Waggoner, Daniel F., and Zha, Tao A. 2003b. Likelihood preserving normalization in multiple equation models. Journal of Econometrics 114: 329–47.
Western, Bruce, and Jackman, Simon. 1994. Bayesian inference for comparative research. American Political Science Review 88(2): 412–23.
Williams, John T. 1990. The political manipulation of macroeconomic policy. American Political Science Review 84(3): 767–95.
Williams, John T. 1993a. Dynamic change, specification uncertainty, and Bayesian vector autoregression analysis. Political Analysis 4: 97125.
Williams, John T. 1993b. What goes around comes around: Unit root tests and cointegration. Political Analysis 1: 229–35.
Williams, John T., and Collins, Brian K. 1997. The political economy of corporate taxation. American Journal of Political Science 41(1): 208–44.
Wilson, Sven E., and Butler, Daniel M. 2007. A lot more to do: The sensitivity of time-series cross-section analyses to simple alternative specifications. Political Analysis 15(2): 101–23.
Zellner, A., and Siow, A. 1980. Posterior odds ratios for selected regression hypotheses. In Bayesian statistics: Proceedings of the First International Meeting Held in Valencia, eds. Bernardo, J. M., DeGroot, M. H., Lindley, D. V., and Smith, A. F. M., 585603. Valencia, Spain: Valencia University Press.
Zha, Tao A. 1998. A dynamic multivariate model for the use of formulating policy. Economic Review (Federal Reserve Bank of Atlanta) 83(1): 1629.
Zha, Tao A. 1999. Block recursion and structural vector autoregression. Journal of Econometrics 90: 291316.
MathJax
MathJax is a JavaScript display engine for mathematics. For more information see http://www.mathjax.org.

Modeling Macro-Political Dynamics

  • Patrick T. Brandt (a1) and John R. Freeman (a2)

Metrics

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed