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Party Voting in the United States Congress

Published online by Cambridge University Press:  27 January 2009

Samuel C. Patterson  and
Gregory A. Caldeira
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Extract

By the standard of most European parliaments, levels of party voting in the United States Congress are relatively low. Nevertheless, party voting does occur in the House of Representatives and the Senate. In the American context, a party vote occurs when majorities of the two congressional parties, the Democrats and the Republicans, oppose one another. The authors construct measurements of levels of party voting in Congress in the years after the Second World War. They then develop a model to test the effects of a number of independent variables that influence fluctuations in party voting levels over time. The study models the time series for party voting and demonstrates striking differences between the House and Senate in the correlates of partisan cleavage.

Type
Articles
Information
British Journal of Political Science , Volume 18 , Issue 1 , January 1988 , pp. 111 - 131
Copyright
Copyright © Cambridge University Press 1988

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References

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34 The analysis of these data was conducted by calculating the ordinary least squares (OLS) parameter estimates adjusted for autocorrelation in the series of observations. The method of estimation we used is the ‘two-step full transformation’ approach described in Gallant, A. R. and Goebel, J. J., ‘Nonlinear Regression with Autoregressive Errors’, Journal of the American Statistical Association, 71 (1976), 961–7CrossRefGoogle Scholar. In accord with this method we first estimated the model with OLS; then we computed the autocovariances of the residuals from the OLS regression. Estimates of the autoregressive parameters were obtained, and the variables from the original data were transformed by the appropriate autoregressive model. Then, using the transformed data, the OLS regression was re-estimated. These procedures are implemented in the software package, SAS. This method is equivalent to what some refer to as ‘generalized least-squares’; see Ostrom, Charles W. Jr, Time Series Analysis: Regression Techniques (Beverly Hills, Calif.: Sage Publications, 1978).Google Scholar