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Modeling Equilibrium Relationships: Error Correction Models with Strongly Autoregressive Data

Published online by Cambridge University Press:  04 January 2017

Suzanna De Boef
Suzanna De Boef*
Affiliation:
The Pennsylvania State University, Department of Political Science, 107 Burrowes Building, University Park, PA 16802. e-mail: sdeboef@psu.edu
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Abstract

Political scientists often argue that political processes move together in the long run. Examples include partisanship and government approval, conflict and cooperation among countries, public policy sentiment and policy activity, economic evaluations and economic conditions, and taxing and spending. Error correction models and cointegrating relationships are often used to characterize these equilibrium relationships and to test hypotheses about political change. Typically the techniques used to estimate equilibrium relationships are based on the statistical assumption that the processes have permanent memory, implying that political experiences cumulate. Yet many analysts have argued that this is not a reasonable theoretical or statistical assumption for most political time series. In this paper I examine the consequences of assuming permanent memory when data have long but not permanent memory. I focus on two commonly used estimators: the Engle-Granger two-step estimator and generalized error correction. In my analysis I consider the important role of simultaneity and discuss implications for the conclusions political scientists have drawn about the nature, even the existence, of equilibrium relationships between political processes. I find that even small violations of the permanent memory assumption can present substantial problems for inference on long-run relationships in situations that are likely to be common in applied work in all fields and suggest ways that analysts should proceed.

Type
Research Article
Information
Political Analysis , Volume 9 , Issue 1 , 2001 , pp. 78 - 94
Copyright
Copyright © 2001 by the Society for Political Methodology 

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References

Abramson, Paul R., and Ostrom, Charles W. 1992. “Question Wording and Macropartisanship.” American Political Science Review 86: 475–486.Google Scholar
Alvarez, R. Michael, and Katz, Jonathan. 1996. “Integration and Political Science Data.” Paper presented at the annual meeting of the Midwest Political Science Association.Google Scholar
Banerjee, Anindya, Galbraith, John W., and Dolado, Juan. 1990. “Dynamic Specification with the General Error-Correction Form.” Oxford Bulletin of Economics and Statistics 52: 95–104.Google Scholar
Banerjee, Anindya, Dolado, Juan, Galbraith, John, and Hendry, David F. 1993. Cointegration, Error Correction, and the Econometric Analysis of Non-Stationary Data. Oxford: Oxford University Press.Google Scholar
Beck, Nathaniel. 1991. “Comparing Dynamic Specifications: The Case of Presidential Approval.” Political Analysis 3: 51–87.Google Scholar
Beck, Nathaniel. 1992. “The Methodology of Cointegration.” Political Analysis 4: 237–248.Google Scholar
Blough, Stephen R. 1994. “Near Observational Equivalence and Persistence in GNP.” Federal Reserve Bank of Boston Working Paper No. 94-6.Google Scholar
Box-Steffensmeier, Janet M., and Smith, Renee M. 1996. “The Dynamics of Aggregate Partisanship.” American Political Science Review 90: 567–580.CrossRefGoogle Scholar
Cavangh, Christopher L., Elliott, Graham, and Stock, James H. 1995. “Inference in Models with Nearly Integrated Regressors.” Econometric Theory 11: 1131–1147.Google Scholar
Charemza, Wojciech, and Deadman, Derek. 1997. New Directions in Econometric Practice: General to Specific Modelling, Cointegration, and Vector Autoregression, 2nd ed. Cheltenham, UK: Edward Elgar.Google Scholar
Clarke, Harold D., and Stewart, Marianne C. 1994. “Prospections, Retrospections, and Rationality: The ‘Bankers’ Model of Presidential Approval Reconsidered.” American Journal of Political Science 38: 1104–1123.Google Scholar
Davidson, J. E. H., Hendry, David F., Srba, F., and Yeo, S. 1978. “Econometric Modeling of the Aggregate Time-Series Relationship Between Consumers’ Expenditure and Income in the United Kingdom.” Economic Journal 88: 661–692.CrossRefGoogle Scholar
De Boef, Suzanna. 2000a. “Assessing Persistence: From Microfoundations to Macro-persistence.” Electoral Studies 19: 9–30.Google Scholar
De Boef, Suzanna. 2000b. “Testing for Cointegrating Relationships with Near-Integrated Data.” Political Analysis 8: 99–117.Google Scholar
De Boef, Suzanna, and Granato, Jim. 1997. “Near-Integrated Data and the Analysis of Political Relationships.” American Journal of Political Science 41: 619–640.Google Scholar
De Boef, Suzanna, and Granato, Jim. 1999. “Testing for Cointegration when Data Are Near-Integrated.” Political Analysis 8: 99–117.Google Scholar
Durr, Robert H. 1993. “What Moves Policy Sentiment?” American Political Science Review 87: 158–170.Google Scholar
Elliott, Graham. 1995. “On the Robustness of Cointegration Methods When Regressors Almost have Unit Roots,” Discussion Paper. San Diego: Department of Economics, University of California.Google Scholar
Elliott, Graham. 1998. “On the Robustness of Cointegration Methods When Regressors Almost Have Unit Roots.” Econometrica 66: 149–158.Google Scholar
Engle, Robert F., and Granger, C. W. J. 1987. “Cointegration and Error Correction: Representation Estimation and Testing.” Econometrica 55: 251–276.Google Scholar
Engle, Robert F. Hendry, David, and Richard, Jean-Francois. 1983. “Exogeneity,” Econometrica 51: 277–304.CrossRefGoogle Scholar
Green, Donald Philip, Palmquist, Bradley, and Schickler, Eric. 1998. “Macropartisanship: A Replication and Critique.” American Political Science Review 92: 883–899.Google Scholar
Johansen, Soren. 1988. “Statistical Analysis of Cointegration Vectors.” Journal of Economic Dynamics and Control 12: 231–254.Google Scholar
Kauppi, Heikki. 1998. “On the Robustness of Hypothesis Testing Based on the Fully Modified Vector Autoregression When Some Roots are Almost One,” Working paper. Cambridge, MA: Harvard University.Google Scholar
Krause, George A. 1997. “Voters, Information Heterogeneity, and the Dynamics of Aggregate Economic Expectations.” American Journal of Political Science 41: 1170–1200.Google Scholar
Kremers, Jeroen, Ericsson, Neil, and Dolado, Juan. 1992. “The Power of Cointegration Tests.” Oxford Bulletin of Economics and Statistics 54: 325–348.Google Scholar
MacKuen, Michael, Erikson, Robert, and Stimson, James. 1998. “What Moves Macropartisanship? A Response to Green, Palmquist, and Schickler.” American Political Science Review 92: 901–912.Google Scholar
Mebane, Walter R. Jr. 1994. “Fiscal Constraints and Electoral Manipulation in American Social Welfare.” American Political Science Review 88: 77–94.CrossRefGoogle Scholar
Ostrom, Charles, and Smith, Renee. 1992. “Error Correction, Attitude Persistence, and Executive Rewards and Punishments: A Behavioral Theory of Presidential Approval.” Political Analysis 4: 127–183.Google Scholar
Phillips, P. C. B. 1987a. “Time Series Regression with a Unit Root.” Econometrica 55: 277–301.Google Scholar
Phillips, P. C. B. 1987b. “Towards a Unified Asymptotic Theory of Autoregression.” Biometrika 74: 535–548.Google Scholar
Rajmaira, Sheen, and Ward, Michael D. 1990. “Evolving Foreign Policy Norms: Reciprocity in the Superpower Triad.” International Studies Quarterly 34: 457–475.Google Scholar
Saikkonen, P. 1991. “Asymptotically Efficient Estimation of Cointegration Regressions.” Econometric Theory 7: 1–21.Google Scholar
Saikkonen, P. 1992. “Estimation and Testing of Cointegrated Systems by an Autoregressive Approximation.” Econometric Theory 8: 1–27.Google Scholar
Simon, Dennis M., Ostrom, Charles W., and Marra, Robin F. 1991. “The President, Referendum Voting, and Subnational Elections in the United States.” American Political Science Review 85: 1177–1192.Google Scholar
Stock, James H. 1987. “Asymptotic Properties of Least Squares Estimators of Cointegrating Vectors.” Econometrica 55: 1035–1056.Google Scholar
Stock, James H. 1997. “Cointegration, Long-run Comovements, and Long-Horizon Forecasting.” In Advances in Econometrics: Proceedings of the Seventh World Congress of the Econometric Society, eds. Kreps, D. and Wallis, K. F. Cambridge: Cambridge University Press, Vol. III, pp. 34–60.Google Scholar
Stock, James H., and Watson, Mark W. 1996. “Confidence Sets in Regressions with Highly Serially Correlated Regressors,” Working paper. Cambridge, MA: Harvard University.Google Scholar
Todaro, Michael. 1994. Economic Development in the Third World. New York: Longman.Google Scholar
Williams, John T. 1992. “What Goes Around Comes Around: Unit Root Tests and Cointegration.” Political Analysis 4: 229–235.CrossRefGoogle Scholar
Wlezien, Christopher. 1995. “The Public as Thermostat: Dynamics of Preferences for Spending.” American Journal of Political Science 39: 981–1000.Google Scholar
Wlezien, Christopher. 1996. “Dynamics of Representation: The Case of U.S. Spending on Defense.” British Journal of Political Science 26: 81–103.Google Scholar