Hostname: page-component-848d4c4894-4hhp2 Total loading time: 0 Render date: 2024-04-30T19:27:07.805Z Has data issue: false hasContentIssue false
  • English
  • Français

Modeling Macro-Political Dynamics

Published online by Cambridge University Press:  04 January 2017

Patrick T. Brandt  and
John R. Freeman
Patrick T. Brandt*
Affiliation:
School of Economic, Political and Policy Sciences, The University of Texas at Dallas 800 W. Campbell Road, GR 31, Richardson, Texas 75080
John R. Freeman
Affiliation:
Department of Political Science, University of Minnesota, 1414 Social Science Building, 267 19th Ave. South, Minneapolis, MN 55455. e-mail: freeman@umn.edu
*
e-mail: pbrandt@utdallas.edu (corresponding author)
Get access Rights & Permissions [Opens in a new window]

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.

Type
Research Article
Information
Political Analysis , Volume 17 , Issue 2 , Spring 2009 , pp. 113 - 142
Copyright
Copyright © The Author 2009. Published by Oxford University Press on behalf of the Society for Political Methodology 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

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.

References

Alesina, Alberto, and Rosenthal, Howard. 1995. Partisan politics, divided government and the economy. Cambridge: Cambridge University Press.Google Scholar
Alesina, Alberto, Londregan, John, and Rosenthal, Howard. 1993. A model of the political economy of the United States. American Political Science Review 87: 12–33.Google Scholar
Bartels, Larry M. 1991. Instrumental and “quasi-instrumental” variables. American Journal of Political Science 35(3): 777–800.Google Scholar
Bartlett, M. S. 1957. A comment on D. V. Lindley's statistical paradox. Biometrika 44 (3/4): 533–534.Google Scholar
Beck, Nathaniel. 1987. Elections and the fed: Is there a political monetary cycle? American Journal of Political Science 31(1): 194–216.Google Scholar
Bernanke, B. 1986. Alternative explanations of the money-income correlation. Carnegie-Rochester Conference Series on Public Policy 25(1): 49–99.Google Scholar
Bernardo, Jose M. 1979. Reference posterior distributions for Bayesian inference. Journal of the Royal Statistical Society. Series B (Methodological) 41(2): 113–47.Google Scholar
Bernhard, William, and Leblang, David. 2006. Democratic processes and financial markets: Pricing politics. New York: Cambridge University Press.Google Scholar
Blanchard, O., and Quah, D. 1989. The dynamic effects of aggregate demand and supply disturbances. American Economic Review 79: 655–73.Google Scholar
Box-Steffensmeier, Janet M., and Smith, Renee M. 1996. The dynamics of aggregate partisanship. American Political Science Review 90(3): 567–80.Google Scholar
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): 1–36.Google Scholar
Brandt, Patrick T., and Williams, John T. 2007. Multiple time series models. Beverly Hills, CA: Sage.CrossRefGoogle Scholar
Brandt, Patrick T., Colaresi, Michael, and Freeman, John R. 2008. The dynamics of reciprocity, accountability, and credibility. Journal of Conflict Resolution 52: 343–74.Google Scholar
Carlin, Bradley P., and Louis, Thomas A. 2000. Bayes and empirical Bayes methods for data analysis. 2nd ed. Boca Raton, FL: Chapman & Hall/CRC.Google Scholar
Chib, Siddartha. 1995. Marginal likelihood from the Gibbs output. Journal of the American Statistical Association 90(432): 1313–21.Google Scholar
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.Google Scholar
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.Google Scholar
Cortes, Fernando, Przeworski, Adam, and Sprague, John. 1974. System analysis for social scientists. New York: John Wiley and Sons.Google Scholar
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.Google Scholar
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.Google Scholar
De Boef, Suzanna L., and Luke, Keele. 2008. Taking time seriously. American Journal of Political Science 52: 184–200.Google Scholar
Del Negro, Marco, and Schorfheide, Frank. 2004. Priors from general equilibrium models for VARs. International Economic Review 45: 643–73.Google Scholar
Doan, Thomas, Litterman, Robert, and Sims, Christopher. 1984. Forecasting and conditional projection using realistic prior distributions. Econometric Reviews 3: 1–100.Google Scholar
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.Google Scholar
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.CrossRefGoogle Scholar
Erikson, Robert S., MacKuen, Michael B., and Stimson, James A. 2002. The macropolity. New York: Cambridge University Press.Google Scholar
Franzese, Robert F. 2002. Macroeconomic policies of developed democracies, Cambridge studies in comparative politics. New York: Cambridge University Press.Google Scholar
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.Google Scholar
Freeman, John R. 2005. Modeling macropolitics: EITM and Reality. Paper presented at the EITM Workshop, Canadian Political Science Association Meetings. London: Ontario.Google Scholar
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.Google Scholar
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.Google Scholar
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): 680–700.CrossRefGoogle Scholar
Geweke, John. 2005. Contemporary Bayesian econometrics and statistics. Hoboken, NJ: John Wiley & Sons.Google Scholar
Gill, Jeff. 2004. Introducing the special issue of political analysis on Bayesian methods. Political Analysis 12(4): 323–37.Google Scholar
Gill, Jeff. 2007. Bayesian methods: A social and behavioral sciences approach. 2nd edn. Boca Raton, FL: Chapman and Hall.Google Scholar
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.Google Scholar
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): 594–620.Google Scholar
Green, Donald, Palmquist, Bradley, and Schickler, Eric. 1998. Macropartisanship: A replication and critique. American Political Science Review 92(4): 883–900.Google Scholar
Hamilton, James D. 1994. Time series analysis. Princeton, NJ: Princeton University Press.Google Scholar
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: 497–510.Google Scholar
Jackman, Simon. 2004. Bayesian analysis in political science. Annual Reviews of Political Science 7: 483–505.Google Scholar
Jackman, Simon. 2008. Bayesian analysis for the social sciences. Hoboken, NJ: John Wiley & Sons.Google Scholar
Kadane, Joseph B., Chan, Ngai Hang, and Wolfson, Lara J. 1996. Priors for unit root models. Journal of Econometrics 75: 99–111.Google Scholar
Kadiyala, K. Rao, and Karlsson, Sune. 1997. Numerical methods for estimation and inference in Bayesian VAR-model. Journal of Applied Econometrics 12: 99–132.Google Scholar
Kass, Robert E., and Raftery, Adrian E. 1995. Bayes factors. Journal of the American Statistical Association 90(430): 773–95.Google Scholar
Leeper, Eric M., Sims, Christopher A., and Zha, Tao A. 1996. What does monetary policy do? Brookings Papers on Economic Activity 1996(2): 1–63.Google Scholar
Litterman, Robert B. 1980. Techniques for forecasting with vector autoregressions. PhD thesis University of Minnesota.Google Scholar
Litterman, Robert B. 1986. Forecasting with Bayesian vector autoregressions—Five years of experience. Journal of Business, Economics and Statistics 4: 25–38.Google Scholar
Manski, Charles. 1995. Identification problems in the social sciences. Cambridge, MA: Harvard University Press.Google Scholar
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.Google Scholar
Mebane, Walte R. 2000. Coordination, moderation, and institutional balancing in American Presidential and House Elections. American Political Science Review 94: 37–53.Google Scholar
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.Google Scholar
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.Google Scholar
Morris, Irwin L. 2000. Congress, the President, and the Federal Reserve: The politics of American monetary policy-making. Ann Arbor: University of Michigan Press.Google Scholar
Ogata, Ktsuhiko. 1967. State space analysis of control systems. Englewood Cliffs, NJ: Prentice-Hall, Inc.Google Scholar
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.Google Scholar
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.Google Scholar
Poole, Keith. 1998. Recovering a basic space from a set of issue scales. American Journal of Political Science 42: 954–93.Google Scholar
Poole, Keith T., and Rosenthal, Howard. 1997. Congress: A political-economic history of roll call voting. New York: Oxford University Press.Google Scholar
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.Google Scholar
Robertson, John C., and Tallman, Ellis W. 1999. Vector autoregressions: Forecasting and reality. Economic Review (Atlanta Federal Reserve Bank) 84(1): 4–18.Google Scholar
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.Google Scholar
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.CrossRefGoogle Scholar
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.Google Scholar
Sims, Christopher A. 1972. Money, income, and causality. American Economic Review 62: 540–52.Google Scholar
Sims, Christopher A. 1980. Macroeconomics and reality. Econometrica 48(1): 1–48.Google Scholar
Sims, Christopher A. 1986a. Are forecasting models usable for policy analysis? Quarterly Review, Federal Reserve Bank of Minneapolis 10: 2–16.Google Scholar
Sims, Christopher A. 1986b. Specification, estimation, and analysis of macroeconomic models. Journal of Money, Credit and Banking 18(1): 121–6.Google Scholar
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).Google Scholar
Sims, Christopher A., and Zha, Tao A. 1995. Error bands for impulse responses. http://sims.princeton.edu/yftp/ier/ (accessed January 15, 2007).Google Scholar
Sims, Christopher A., and Zha, Tao A. 1998. Bayesian methods for dynamic multivariate models. International Economic Review 39(4): 949–68.Google Scholar
Sims, Christopher A., and Zha, Tao A. 1999. Error bands for impulse responses. Econometrica 67(5): 1113–56.Google Scholar
Theil, Henri. 1963. On the use of incomplete prior information in regression analysis. Journal of the American Statistical Association 58(302): 401–14.Google Scholar
Waggoner, Daniel F., and Zha, Tao A. 2003a. A Gibbs sampler for structural vector autoregressions. Journal of Economic Dynamics & Control 28: 349–66.Google Scholar
Waggoner, Daniel F., and Zha, Tao A. 2003b. Likelihood preserving normalization in multiple equation models. Journal of Econometrics 114: 329–47.Google Scholar
Western, Bruce, and Jackman, Simon. 1994. Bayesian inference for comparative research. American Political Science Review 88(2): 412–23.Google Scholar
Williams, John T. 1990. The political manipulation of macroeconomic policy. American Political Science Review 84(3): 767–95.Google Scholar
Williams, John T. 1993a. Dynamic change, specification uncertainty, and Bayesian vector autoregression analysis. Political Analysis 4: 97–125.Google Scholar
Williams, John T. 1993b. What goes around comes around: Unit root tests and cointegration. Political Analysis 1: 229–35.Google Scholar
Williams, John T., and Collins, Brian K. 1997. The political economy of corporate taxation. American Journal of Political Science 41(1): 208–44.Google Scholar
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.Google Scholar
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., 585–603. Valencia, Spain: Valencia University Press.Google Scholar
Zha, Tao A. 1998. A dynamic multivariate model for the use of formulating policy. Economic Review (Federal Reserve Bank of Atlanta) 83(1): 16–29.Google Scholar
Zha, Tao A. 1999. Block recursion and structural vector autoregression. Journal of Econometrics 90: 291–316.Google Scholar