Hostname: page-component-7bb8b95d7b-qxsvm Total loading time: 0 Render date: 2024-09-16T07:35:24.538Z Has data issue: false hasContentIssue false
  • English
  • Français

An Essay on Cointegration and Error Correction Models

Published online by Cambridge University Press:  04 January 2017

Robert H. Durr
Get access Rights & Permissions [Opens in a new window]

Extract

For political scientists who engage in longitudinal analyses, the question of how best to deal with nonstationary time-series is anything but settled. While many believe that little is lost when the focus of empirical models shifts from the nonstationary levels to the stationary changes of a series, others argue that such an approach erases any evidence of a long-term relationship among the variables of interest. But the pitfalls of working directly with integrated series are well known, and post-hoc corrections for serially correlated errors often seem inadequate. Compounding (or perhaps alleviating, if one believes in the power of selective perception) the difficult question of whether to difference a time-series is the fact that analysts have been forced to rely on subjective diagnoses of the stationarity of their data. Thus, even if one felt strongly about the superiority of one modeling approach over another, the procedure for determining whether that approach is even applicable can be frustrating.

Type
Research Article
Information
Political Analysis , Volume 4 , 1992 , pp. 185 - 228
Copyright
Copyright © by the University of Michigan 1993 

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.)

References

Beck, Nathaniel. 1991. “Comparing Dynamic Specifications: The Case of Presidential Approval.” Political Analysis 3: 51–87.Google Scholar
Box, G. E. P., and Jenkins, G. M. 1976. Time Series Analysis: Forecasting and Control. Rev. ed. San Francisco: Holden-Day.Google Scholar
Breusch, T. S. 1978. “Testing for Autocorrelation in Dynamic Linear Models.” Australian Economic Papers 17: 334–55.CrossRefGoogle Scholar
Citrin, Jack, and Green, Donald Philip. 1990. “The Self-Interest Motive in American Public Opinion.” Research in Micropolitics 3: 1–28.Google Scholar
Dalton, Russell J., 1988. Citizen Politics in Western Democracies: Public Opinion and Political Parties in the United States. Great Britain. West Germany, and France. Chatham, N.J.: Chatham House.Google Scholar
Davidson, J., Hendry, D., Srba, F., and Yeo, S. 1978. “Econometric Modelling of the Aggregate Time Series Relationship between Consumers’ Expenditures and Income in the United Kingdom.” Economic Journal 88: 661–92.CrossRefGoogle Scholar
Dickey, David A., and Fuller, Wayne A. 1979. “Distribution of the Estimators for Autoregressive Time Series with a Unit Root.” Journal of the American Statistical Association 74: 427–31.Google Scholar
Durr, Robert H. 1992. “What Moves Public Opinion? An Analysis of Changes in U.S. Domestic Policy Sentiment.” Ph.D. diss., University of Iowa.Google Scholar
Engle, Robert F., and Granger, Clive W. J. 1987. “Co-Integration and Error Correction: Representation, Estimation, and Testing.” Econometrica 55: 251–76.Google Scholar
Flanagan, Scott. 1987. “Value Change in Industrial Societies.” American Political Science Review 81: 1303–19.Google Scholar
Freeman, John R. 1983. “Granger Causality and the Times Series Analysis of Political Relationships.” American Journal of Political Science 27: 327–58.CrossRefGoogle Scholar
Godfrey, L. G. 1978. “Testing against General Autoregression and Moving Average Error Models when the Regressors Include Lagged Dependent Variables.” Econometrica 37: 424–38.Google Scholar
Granger, Clive W. J. 1963. “Economic Processes Involving Feedback.” Information and Control 6: 28–48.CrossRefGoogle Scholar
Granger, Clive W. J. 1986. “Developments in the Study of Cointegrated Economic Variables.” Oxford Bulletin of Economics and Statistics 48: 213–28.CrossRefGoogle Scholar
Granger, Clive W. J. 1988. “Some Recent Developments in a Concept of Causality.” Journal of Econometrics 39: 199–211.Google Scholar
Granger, Clive W.J., and Newbold, P. 1974. “Spurious Regressions in Econometrics.” Journal of Econometrics 2: 111–20.Google Scholar
Hall, S. G. 1986. “An Application of the Granger and Engle Two-Step Estimation Procedure to United Kingdom Aggregate Wage Data.” Oxford Bulletin of Economics and Statistics 48: 229–39.CrossRefGoogle Scholar
Hall, S. G., and Henry, S. G. B. 1988. Macroeconomic Modelling. Amsterdam: North-Holland.Google Scholar
Inglehart, Ronald. 1971. “The Silent Revolution in Europe: Intergenerational Change in Industrial Societies.” American Political Science Review 65: 991–1017.CrossRefGoogle Scholar
Katona, George. 1975. Psychological Economics. New York: Elsevier.Google Scholar
King, Gary. 1989. Unifying Political Methodology: The Likelihood Theory of Statistical Inference. Cambridge: Cambridge University Press.Google Scholar
MacKinnon, James G. 1990. “Critical Values for Cointegration Tests.” Department of Economics Discussion Paper. University of California at San Diego.Google Scholar
Maslow, Abraham H. 1970. Motivation and Personality. 2d ed. New York: Wiley.Google Scholar
Mills, Terrence C. 1990. Time Series Techniques for Economists. Cambridge: Cambridge University Press.Google Scholar
Ostrom, Charles W. Jr. 1978. Time Series Analysis: Regression Techniques. Beverly Hills: Sage.Google Scholar
Sirgy, M. Joseph. 1986. “A Quality-of-Life Theory Derived from Maslow's Developmental Perspective: Quality Is Related to a Progressive Satisfaction of Needs, Lower Order and Higher.” American Journal of Economics and Sociology 45: 329–42.Google Scholar
Stimson, James A. 1991a. “Dynamic Representation.” University of Iowa. Typescript.Google Scholar
Stimson, James A. 1991b. Public Opinion in America: Moods, Cycles, and Swings. Boulder, Colo.: Westview Press.Google Scholar
Stock, James H., and Watson, Mark W. 1988. “Variable Trends in Economic Time Series.” Journal of Economic Perspectives 2: 147–74.CrossRefGoogle Scholar