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Time Series Intervals and Statistical Inference: The Effects of Temporal Aggregation on Event Data Analysis

Published online by Cambridge University Press:  04 January 2017

Stephen M. Shellman
Stephen M. Shellman*
Affiliation:
Department of Government, The College of William and Mary, Williamsburg, VA 23187-8795. e-mail: [email protected]
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Abstract

While many areas of research in political science draw inferences from temporally aggregated data, rarely have researchers explored how temporal aggregation biases parameter estimates. With some notable exceptions (Freeman 1989, Political Analysis 1:61–98; Alt et al. 2001, Political Analysis 9:21–44; Thomas 2002, “Event Data Analysis and Threats from Temporal Aggregation”) political science studies largely ignore how temporal aggregation affects our inferences. This article expands upon others' work on this issue by assessing the effect of temporal aggregation decisions on vector autoregressive (VAR) parameter estimates, significance levels, Granger causality tests, and impulse response functions. While the study is relevant to all fields in political science, the results directly apply to event data studies of conflict and cooperation. The findings imply that political scientists should be wary of the impact that temporal aggregation has on statistical inference.

Type
Replications and Extensions
Information
Political Analysis , Volume 12 , Issue 1 , Winter 2004 , pp. 97 - 104
Copyright
Copyright © Society for Political Methodology 2004 

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References

Alt, James, King, Gary, and Signorino, Curtis S. 2001. “Aggregation among Binary, Count and Duration Models: Estimating the Same Quantities from Different Levels of Data.” Political Analysis 9: 21—44.CrossRefGoogle Scholar
Azar, Edward E. 1982. The Codebook of the Conflict and Peace Data Bank (COPDAB). Unpublished. University of North Carolina at Chapel Hill.Google Scholar
Dixon, William J. 1988. “The Discrete Sequential Analysis of Dynamic International Behavior.” Quality and Quantity 22: 239—254.CrossRefGoogle Scholar
Enders, Walter. 1995. Applied Econometric Time Series. New York: Wiley.Google Scholar
Franzosi, Roberto. 1995. The Puzzle of Strikes: Class and State Strategies in Postwar Italy. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Freeman, John R. 1989. “Systematic Sampling, Temporal Aggregation, and the Study of Political Relationships.” Political Analysis 1: 61—98.CrossRefGoogle Scholar
Goldstein, Joshua S. 1991. “Reciprocity in Superpower Relations: An Empirical Analysis.” International Studies Quarterly 35: 195—210.Google Scholar
Goldstein, Joshua S. 1992. “A Conflict-Cooperation Scale for WEIS Events Data.” Journal of Conflict Resolution 36: 369—385.Google Scholar
Goldstein, Joshua S., and Freeman, John R. 1990. Three-Way Street: Strategic Reciprocity in World Politics. Chicago, IL: University of Chicago Press.Google Scholar
Granger, C. W. J., and Siklos, Pierre L. 1995. “Systematic Sampling, Temporal Aggregation, Seasonal Adjustment, and Cointegration: Theory and Evidence.” Journal of Econometrics 66: 357—369.Google Scholar
Leeds, Brett Ashley, Davis, David R., Moore, Will H., and McHorney, Christopher. 1995. The Intranational Political Interactions (IPI) Codebook. (Available from http://garnet.acns.fsu.edu/∼whmoore/ipi/codebook.html.)Google Scholar
Marcellino, Massimiliano. 1999. “Some Consequences of Temporal Aggregation in Empirical Analysis.” Journal of Business and Economic Statistics 17: 129—136.Google Scholar
Moore, Will H. 1998. “Repression and Dissent: Substitution, Context, and Timing.” American Journal of Political Science 42: 851—873.CrossRefGoogle Scholar
Pierse, R. G., and Snell, A. J. 1995. “Temporal Aggregation and the Power of Tests for a Unit Root.” Journal of Econometrics 65: 333—345.Google Scholar
Rossana, Robert J., and Seater, John J. 1995. “Temporal Aggregation and Economic Time Series.” Journal of Business and Economic Statistics 13: 441—451.Google Scholar
Shellman, Stephen M. 2003. “Taking Turns: A Theory of Government-Dissident Interactions.” Unpublished dissertation. Tallahassee: Florida State University.Google Scholar
Shellman, Stephen M. 2004. “Measuring the Intensity of Intranational Political Interactions Events Data: Two Interval-like Scales.” International Interactions in press.Google Scholar
Sims, C. A. 1971. “Discrete Approximations to Continuous Time Distributed Lags in Econometrics.” Econometrica 39: 545—563.Google Scholar
Sims, C. A. 1980. “Macroeconomics and Reality.” Econometrica 48: 1—49.CrossRefGoogle Scholar
Thomas, G. Dale. 2002. “Event Data Analysis and Threats from Temporal Aggregation.” Presented at the Florida Political Science Association Meeting, Sarasota, FL, March 8.Google Scholar
Wei, William W.S. 1982. “The Effects of Systematic Sampling and Temporal Aggregation on Causality—A Cautionary Note.” Journal of the American Statistical Association 77: 316—319.Google Scholar
Zellner, Arnold, and Montmarquette, Claude. 1971. “A Study of Some Aspects of Temporal Aggregation Problems in Econometric Analyses.” Review of Economics and Statistics 53: 335—342.Google Scholar
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