Dynamics of Aggregate Partisanship

Smith, Renee

doi:10.3886/icpsr01119.v1

Cited by 5 publications

(9 citation statements)

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“…Having discussed the findings of this study, we caution readers to remember that this analysis has been conducted with aggregate data from Appalachian counties. Thus, readers should refrain from making all but minimal inference concerning the voting behavior of individuals in Appalachia because such inference might lead readers to commit the infamous "ecological fallacy" (Box-Steffensmeier and Smith 1996). 5 In order to gain a sound understanding of the voting behavior of those individuals who are labeled "Appalachian," we encourage further study of this phenomenon at the individual level.…”

Section: Discussionmentioning

confidence: 99%

Economic Status versus Same‐Sex Marriage Referenda in the 2004 Democratic Presidential Vote in “Appalachian” Counties

LaFrance

Frederick

2007

Politics & Policy

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Using multivariate regression analysis, this study compares the relative effects of traditional aggregate rational choice electoral behavior with a more recent perspective focusing on the preservation of group norms and group morality in the context of county economic classification and the presence or absence of same‐sex marriage referenda in the 2004 presidential election in Appalachian counties. Our findings suggest that economic factors, in the aggregate, retain the explanatory value commonly afforded them, but the presence of same‐sex marriage referenda fails to conform with the expectations of the group norm/morality perspective of rational choice behavior. We discuss how this study fits with other analyses of same‐sex marriage referenda in the 2004 election. Our findings suggest that the region can no longer, if it ever was, be considered politically and socially cohesive.

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Section: Discussionmentioning

confidence: 99%

Economic Status versus Same‐Sex Marriage Referenda in the 2004 Democratic Presidential Vote in “Appalachian” Counties

LaFrance

Frederick

2007

Politics & Policy

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“…In important early contributions, Box-Steffensmeier and Smith (1996) and Byers et al (1997Byers et al ( , 2002 showed that political popularity, as measured by public opinion polls, can be modelled as fractional time series processes. The fractional (or fractionally integrated or just integrated) time series models are based on the fractional difference operator…”

Section: Fractional Integration Polling Data and Summary Statisticsmentioning

confidence: 99%

“…The above theoretical argument in favour of modelling opinion poll data as fractional time series has been supported in empirical work by a large number of researchers. For example, Box-Steffensmeier and Smith (1996) estimated fractional models for US data, Byers et al (1997) and Dolado et al (2002) analysed UK data, Dolado et al (2003) analysed Spanish data, Byers et al (2000) analysed data for eight countries and Jones et al (2014) analysed Canadian data. All found strong evidence in support of fractional integration with estimates of d around 0.6-0.8.…”

Section: Fractional Integration Polling Data and Summary Statisticsmentioning

confidence: 99%

“…The fractional integration behaviour of political opinion polling data has been well established in the literature, albeit for time series at lower frequencies (monthly or quarterly), e.g. Box-Steffensmeier and Smith (1996), Byers et al (1997Byers et al ( , 2000Byers et al ( , 2002, Dolado et al (2002Dolado et al ( , 2003 and Jones et al (2014). For a more general reference on fractional integration methods in political time series data, see Box-Steffensmeier and Tomlinson (2000) and ; both in the special issue of Electoral Studies edited by .…”

Section: Introductionmentioning

confidence: 99%

“…When polls are conducted at regular intervals, it seems natural to use a statistical model to extract the full potential of the information that is contained in these time series of poll results by using them to forecast public opinion beyond the most recent poll date. However, long time series of poll data are scarce, and, to the best of our knowledge, all previous studies that have analysed time series of political opinion polls have used data that were observed at the monthly frequency or lower; see, for example, Box-Steffensmeier and Smith (1996), Byers et al (1997Byers et al ( , 2000Byers et al ( , 2002, Dolado et al (2002Dolado et al ( , 2003 and Jones et al (2014). Authors of these studies have noted that an ideal data set would have all observations contained within a single government regime spanning only one political cycle, while providing a sufficiently large sample to conduct meaningful statistical analysis.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting Daily Political Opinion Polls Using the Fractionally Cointegrated Vector Auto-Regressive Model

Nielsen

Shibaev²

2016

Journal of the Royal Statistical Society Series A: Statistics in Society

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Summary.We examine forecasting performance of the recent fractionally cointegrated vector auto-regressive (FCVAR) model.We use daily polling data of political support in the UK for 2010-2015 and compare with popular competing models at several forecast horizons. Our findings show that the four variants of the FCVAR model considered are generally ranked as the top four models in terms of forecast accuracy, and the FCVAR model significantly outperforms both univariate fractional models and the standard cointegrated vector auto-regressive model at all forecast horizons.The relative forecast improvement is higher at longer forecast horizons, where the root-mean-squared forecast error of the FCVAR model is up to 15% lower than that of the univariate fractional models and up to 20% lower than that of the cointegrated vector autoregressive model. In an empirical application to the 2015 UK general election, the estimated common stochastic trend from the model follows the vote share of the UK Independence Party very closely, and we thus interpret it as a measure of Euroscepticism in public opinion rather than an indicator of the more traditional left-right political spectrum. In terms of prediction of vote shares in the election, forecasts generated by the FCVAR model leading to the election appear to provide a more informative assessment of the current state of public opinion on electoral support than the hung Parliament prediction of the opinion poll.

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Advances in Bayesian Time Series Modeling and the Study of Politics: Theory Testing, Forecasting, and Policy Analysis

Brandt¹,

Freeman²

2006

Polit. anal.

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Bayesian approaches to the study of politics are increasingly popular. But Bayesian approaches to modeling multiple time series have not been critically evaluated. This is in spite of the potential value of these models in international relations, political economy, and other fields of our discipline. We review recent developments in Bayesian multi-equation time series modeling in theory testing, forecasting, and policy analysis. Methods for constructing Bayesian measures of uncertainty of impulse responses (Bayesian shape error bands) are explained. A reference prior for these models that has proven useful in short-and medium-term forecasting in macroeconomics is described. Once modified to incorporate our experience analyzing political data and our theories, this prior can enhance our ability to forecast over the short and medium terms complex political dynamics like those exhibited by certain international conflicts. In addition, we explain how contingent Bayesian forecasts can be constructed, contingent Bayesian forecasts that embody policy counterfactuals. The value of these new Bayesian methods is illustrated in a reanalysis of the Israeli-Palestinian conflict of the 1980s.

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Dynamics of Aggregate Partisanship

Cited by 5 publications

References 0 publications

Economic Status versus Same‐Sex Marriage Referenda in the 2004 Democratic Presidential Vote in “Appalachian” Counties

Economic Status versus Same‐Sex Marriage Referenda in the 2004 Democratic Presidential Vote in “Appalachian” Counties

Forecasting Daily Political Opinion Polls Using the Fractionally Cointegrated Vector Auto-Regressive Model

Advances in Bayesian Time Series Modeling and the Study of Politics: Theory Testing, Forecasting, and Policy Analysis

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