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Progress in the Study of Nonstationary Political Time Series: A Comment

Published online by Cambridge University Press:  04 January 2017

John R. Freeman
John R. Freeman*
Affiliation:
Department of Political Science, University of Minnesota, 1414 Social Sciences Building, 267 19th Ave. South, Minneapolis, MN 55455, USA
*
e-mail: [email protected] (corresponding author)
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Extract

Cointegration was introduced to our discipline by Renée Smith and Charles Ostrom Jr. and by Robert Durr more than two decades ago at political methodology meetings in Washington University�St. Louis and Florida State University. Their articles, along with comments by Neal Beck and John T. Williams, were published in a symposium like this one in the fourth volume of Political Analysis. Keele, Lin, and Webb (2016; hereafter KLW) and Grant and Lebo (2016; hereafter GL) show how, in the years that followed, cointegration was further evaluated by political scientists, and the related idea of error correction subsequently was applied.

Have the last twenty-plus years witnessed significant progress in modeling nonstationary political time series? In some respects, the answer is yes. The present symposium represents progress in understanding equation balance, analyzing bounded variables, and decomposing short- and longterm causal effects. In these respects KLW's and GL's articles deserve wide dissemination. But KLW and GL leave important methodological issues unresolved. They do not address some critical methodological challenges. From a historical perspective, the present symposium shows that we have made relatively little progress in modeling nonstationary political time series.

Type
Time Series Symposium
Information
Political Analysis , Volume 24 , Issue 1 , Winter 2016 , pp. 50 - 58
Copyright
Copyright © The Author 2016. Published by Oxford University Press on behalf of the Society for Political Methodology 

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Footnotes

Author's note: The author thanks Patrick Brandt for discussions about the original and current symposia and also about the strengths and weaknesses of the Bayesian approach to this subject. He also thanks Janet Box-Steffensmeier and Jim Granato for their comments. The author alone is responsible for the paper's content.

References

Baltagi, Badi H., and Kao, C. 2000. Nonstationary panels, cointegration in panels, and dynamic panels: A survey. Advances in Econometrics 15:7–51.Google Scholar
Banerjee, Anindya, Dolado, Juan J., Galbraith, John W., and Hendry, David F. 1993. Cointegration, error correction, and the econometric analysis of non- stationary data. New York: Oxford University Press.Google Scholar
Beck, Nathaniel. 1993. The methodology of cointegation. Political Analysis 4:237–48.Google Scholar
Beck, Nathaniel, and Katz, Jonathan N. 2011. Modeling dynamics in time-series-cross section political economy data. Annual Review of Political Science 14:331–52.Google Scholar
Bernhard, William, and Leblang, David. 2006. Democratic processes and financial markets: Pricing politics. New York: Cambridge University Press.Google Scholar
Binder, Michael, Hsiao, Cheng, and Hashem Pesaran, M. 2005. Estimation and inference in short panel vector autoregressions with unit roots and cointegration. Econometric Theory 21:795–837.Google Scholar
Brandt, Patrick T. 2009. Empirical regime-specific models of international, inter-group conflict and politics. Paper presented at the Annual Meeting of the Midwest Political Science Association, Chicago, Illinois.Google Scholar
Brandt, Patrick T., Colaresi, Michael, and Freeman, John R. 2008. The dynamics of reciprocity, accountability and credibility. Journal of Conflict Resolution 52(3): 343–71.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 Freeman, John R. 2009. Modeling macropolitical dynamics. Political Analysis 17(2): 113–42.CrossRefGoogle Scholar
Brandt, Patrick T., and Williams, John T. 2007. Multiple time series models. Thousand Oaks, CA: Sage Publications.Google Scholar
Box-Steffensmeier, Janet M., Freeman, John R., Hitt, Matthew P., and Pevehouse, Jon C. W. 2015. Time Series Analysis for the Social Sciences. New York: Cambridge University Press.Google Scholar
DeBoef, Suzanna, and Keele, Luke. 2008. Taking time seriously. American Journal of Political Science 52(1): 184–200.CrossRefGoogle Scholar
DeVries, Casper G. 1992. Stylized facts of nominal exchange rate returns. In The handbook of international macroeconomics, ed. van der Ploeg, Frederick. London, England: Blackwell Publishers.Google Scholar
Durr, Robert H. 1993a. An essay on cointegration and error correction models. Political Analysis 4:185–228.CrossRefGoogle Scholar
Durr, Robert H. 1993b. Of forest and trees. Political Analysis 4:249–54.Google Scholar
Enders, Walter. 2010. Applied econometric time series, 3rd ed. New York: John Wiley and Sons, Inc.Google Scholar
Erikson, Robert, MacKuen, Michael, and Stimson, James. 1998. What moves macropartisanship? American Political Science Review 92(4): 901–12.Google Scholar
Freeman, John R. 2014. Research note: Can time series methods be used to detect path dependence? Unpublished manuscript. Minneapolis: University of Minnesota. https://sites.google.com/a/umn.edu/john-freeman/.Google Scholar
Freeman, John R., and Alt, James. 1994. The politics of public and private investment in Britain. In The comparative political economy of the welfare state, eds. Jonoski, Thomas and Hicks, Alexander M. New York: Cambridge University Press.Google Scholar
Freeman, John R., Houser, Daniel, Kellstedt, Paul M., and Williams, John T. 1998. Long-memoried processes, unit roots and cointegration. American Journal of Political Science 42(4): 1289–327.Google Scholar
Frühwirth-Schnatter, Sylvia. 2006. Finite mixture and Markov switching models. New York: Springer.Google Scholar
Granato, Jim. 1991. An agenda for econometric model building. In Political analysis, Vol. 3, ed. Stimson, James. Ann Arbor, Michigan: University of Michigan Press: 123–54.Google Scholar
Grant, Tayler, and Lebo, Matthew J. 2016. Error correction methods with political time series. Political Analysis 24:3–30.Google Scholar
Hays, Jude, Freeman, John R., and Nesseth, Hans. 2003. Exchange rate volatility and democratization in emerging market countries. International Studies Quarterly 47:203–28.Google Scholar
Hendry, David F. 1995. Dynamic econometrics. New York: Oxford University Press.Google Scholar
27If one knows the order of integration of all the variables and exactly which variables are cointegrated, in theory, given the nuisance parameters in the system, the appropriate multivariate nonstandard distribution could be derived and sound inferences could be made (Sims, Stock, and Watson 1990). If the orders of integration and cointegrating relationships are not known, one could pretest for these things and employ (simulate) the relevant nonstandard multivariate distribution. Alternatively, on the basis of one's beliefs about nonstationarity in the system, one could set the values of the hyperparameters such as λ1, μ 5,6 in the Sims-Zha prior to reflect those beliefs, and, in effect create departures from what would otherwise be a proper, multivariate t posterior distribution. What is at issue is whether mistaken inferences are more or less likely if one adopts this Bayesian approach.Google Scholar
Keele, Luke, Linn, Suzanna, and Webb, Clayton M. 2016. Treating time with all due seriousness. Political Analysis 24:31–41.Google Scholar
Lebo, Matthew J., McGlynn, Adam J., and Kroger, Gregory. 2007. Strategic party government: Party influence in Congress 1979–2000. American Journal of Political Science 51(3): 464–81.Google Scholar
Maddala, G. S., and Kim, In-Moo. 1998. Unit roots, cointegration, and structural change. New York: Cambridge University Press.Google Scholar
Nickell, Stephen. 1985. Error correction, partial adjustment and all that: An expository note. Oxford Bulletin of Economics and Statistics 47(2): 119–29.Google Scholar
Ostrom, Charles W., and Smith, Renée M. 1993. Error correction, attitude persistence, and executive rewards and punishments: A behavioral theory of presidential approval. Political Analysis 4:127–84.Google Scholar
Park, Jong Hee. 2010. Structural change in the U.S. president's use of force abroad. American Journal of Political Science 54(3).Google Scholar
Perron, Pierre. 1989. The great crash, the oil price shock and the unit root hypothesis. Econometrica 57:1361–401.Google Scholar
Phillips, Peter C.B., and Moon, H. R. 2000. Nonstationary panel data analysis: An overview of some recent developments. Econometric Reviews 19:263–86.Google Scholar
Sattler, Thomas, Freeman, John R., and Brandt, Patrick T. 2010. Democratic accountability in open economies. Quarterly Journal of Political Science 5:71–97.Google Scholar
Sims, Christopher A., Stock, James H., and Watson, Mark W. 1990. Inferences in linear time series models with some unit roots. Econometrica 58:113–44.Google Scholar
Smith, Renée. 1993. Error correction, attractors, and cointegration: substantive and methodological issues. Political Analysis 4:249–54.Google Scholar
Williams, John T. 1993a. Dynamic change, specification uncertainty, and Bayesian vector autoregression. Political Analysis 4:97–126.CrossRefGoogle Scholar
Williams, John T. 1993b. What goes around comes around: Unit root tests and cointegration. Political Analysis 4:229–38.Google Scholar
Williams, John T., and Huckfeldt, Robert. 1996. Empirically discriminating between chaotic and stochastic time series. Political Analysis 6:125–54.Google Scholar
Williams, John T., and McGinnis, Michael. 1998. Reaction in a rational expectations arms race model of U.S.-Soviet rivalry. American Journal of Political Science 32:968–95.Google Scholar
Wood, B. Dan, and Jordan, Soren. 2012. Electoral participation: A cause or a consequence of elite polarization. Paper presented at the annual meeting of the Midwest Political Science Association, Chicago, Illinois.Google Scholar
Zeitzoff, Thomas. 2011. Using social media to measure conflict dynamics: An application to the 2008–2009 Gaza conflict. Journal of Conflict Resolution 55(6): 938–69.Google Scholar