Hostname: page-component-586b7cd67f-2plfb Total loading time: 0 Render date: 2024-11-22T02:21:39.887Z Has data issue: false hasContentIssue false
  • English
  • Français

Comparing Dynamic Pies: A Strategy for Modeling Compositional Variables in Time and Space

Published online by Cambridge University Press:  27 November 2017

Christine S. Lipsmeyer ,
Andrew Q. Philips ,
Amanda Rutherford  and
Guy D. Whitten
Get access Rights & Permissions [Opens in a new window]

Abstract

Across a broad range of fields in political science, there are many theoretically interesting dependent variables that can be characterized as compositions. We build on recent work that has developed strategies for modeling variation in such variables over time by extending them to models of time series cross-sectional data. We discuss how researchers can incorporate the influence of contextual variables and spatial relationships into such models. To demonstrate the utility of our proposed strategies, we present a methodological illustration using an analysis of budgetary expenditures in the US states.

Type
Original Articles
Information
Political Science Research and Methods , Volume 7 , Issue 3 , July 2019 , pp. 523 - 540
Copyright
© The European Political Science Association 2017 

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.)

Footnotes

*

Christine S. Lipsmeyer is an Associate Professor in the Department of Political Science, Texas A&M University, 2010 Allen Building, 4348 TAMU, College Station, TX 77843-4348 ([email protected]). Andrew Q. Philips is an Assistant Professor in the Department of Political Science, University of Colorado Boulder, UCB 333, Boulder, CO 80309-0333 ([email protected]). Amanda Rutherford is an Assistant Professor in the School of Public and Environmental Affairs, Indiana University, 1315 E 10th Street, Bloomington, IN 47405-1106 ([email protected]). Guy D. Whitten is a Professor in the Department of Political Science, Texas A&M University, 2010 Allen Building, 4348 TAMU, College Station, TX 77843-4348 ([email protected]). Earlier versions of this paper were presented at the 2015 conference on “Innovations in Comparative Political Methodology” at Texas A&M University, the 2015 MPSA meetings, and at the University of São Paulo. The authors thank these audiences for their helpful comments. To view supplementary material for this article, please visit https://doi.org/10.1017/psrm.2017.39

References

Adolph, Christopher. 2013. Bankers, Bureaucrats, and Central Bank Politics: The Myth of Neutrality. New York: Cambridge University Press.Google Scholar
Aitchison, John. 1982. ‘The Statistical Analysis of Compositional Data’. Journal of the Royal Statistical Society. Series B (Methodological) 44(2):139–177.Google Scholar
Aitchison, John. 1983. ‘Principal Component Analysis of Compositional Data’. Biometrika 70(1):57–65.Google Scholar
Aitchison, John. 1986. The Statistical Analysis of Compositional Data. New York: Chapman & Hall Ltd.Google Scholar
Beck, Nathaniel, Gleditsch, Kristian Skrede, and Beardsley, Kyle. 2006. ‘Space is More Than Geography: Using Spatial Econometrics in the Study of Political Economy’. International Studies Quarterly 50(1):27–44.Google Scholar
Breunig, Christian, and Busemeyer, Marius. 2012. ‘Fiscal Austerity and the Trade-Off Between Public Investment and Social Spending’. Journal of European Public Policy 19:921–938.Google Scholar
Clark, Tom S., and Linzer, Drew A.. 2015. ‘Should I Use Fixed or Random Effects?’. Political Science Research and Methods 3(2):399–408.Google Scholar
Darmofal, David. 2015. Spatial Analysis for the Social Sciences. New York: Cambridge University Press.Google Scholar
Drukker, David M., Prucha, Ingmar R., and Raciborski, Rafal. 2013. ‘Maximum Likelihood and Generalized Spatial Two-Stage Least-Squares Estimators for a Spatial-Autoregressive Model With Spatial-Autoregressive Disturbances’. The Stata Journal 13(2):221–241.Google Scholar
Franzese, Robert J., and Hays, Jude C.. 2007. ‘Spatial Econometric Models of Cross-Sectional Interdependence in Political Science Panel and Time-Series-Cross-Section Data’. Political Analysis 15(2):140–164.Google Scholar
Halleck Vega, Solmaria, and Elhorst, J. Paul. 2015. ‘The SLX Model’. Journal of Regional Science 55(3):339–363.Google Scholar
Jackson, John E. 2002. ‘A Seemingly Unrelated Regression Model for Analyzing Multiparty Elections’. Political Analysis 10(1):49–65.Google Scholar
Katz, Jonathan N., and King, Gary. 1999. ‘A Statistical Model for Multiparty Electoral Data’. American Political Science Review 93(1):15–32.Google Scholar
Klarner, Carl. 2013a. ‘State Economic Data’. IQSS Dataverse Network. Available at http://hdl.handle.net/1902.1/20404, accessed 15 August 2014.Google Scholar
Klarner, Carl. 2013b. ‘State Partisan Balance Data, 1937–2011’. IQSS Dataverse Network. Available at http://hdl.handle.net/1902.1/20403, accessed 15 August 2014.Google Scholar
Mikhailov, Nikolai, Niemi, Richard G., and Weimer, David L.. 2002. ‘Application of Theil Group Logit Methods to District-Level Vote Shares: Tests of Prospective and Retrospective Voting in the 1991, 1993, and 1997 Polish Elections’. Electoral Studies 21(4):631–648.Google Scholar
Neumayer, Eric, and Plümper, Thomas. 2012. ‘Conditional Spatial Policy Dependence Theory and Model Specification’. Comparative Political Studies 45(7):819–849.Google Scholar
Philips, Andrew, Rutherford, Amanda, and Whitten, Guy D.. 2016a. ‘Dynamic Pie: A Strategy for Modeling Trade-Offs in Compositional Variables Over Time’. American Journal of Political Science 60(1):268–283.Google Scholar
Philips, Andrew Q., Rutherford, Amanda, and Whitten, Guy D.. 2015. ‘The Dynamic Battle for Pieces of Pie—Modeling Party Support in Multi-Party Nations’. Electoral Studies 39:264–274.Google Scholar
Philips, Andrew Q., Rutherford, Amanda, and Whitten, Guy D.. 2016b. ‘Dynsimpie: A Program to Examine Dynamic Compositional Dependent Variables’. Stata Journal 16(3):662–677.Google Scholar
Plümper, Thomas, and Troeger, Vera E.. 2007. ‘Efficient Estimation of Time-Invariant and Rarely Changing Variables in Finite Sample Panel Analyses With Unit Fixed Effects’. Political Analysis 15(2):124–139.Google Scholar
Tomz, Michael, Wittenberg, Jason, and King, Gary. 2003. ‘Clarify: Software for Interpreting and Presenting Statistical Results’. Journal of Statistics Software 8(1):1–30.Google Scholar
Tomz, Michael, Tucker, Joshua, and Wittenberg, Jason. 2002. ‘An Easy and Accurate Regression Model for Multiparty Electoral Data’. Political Analysis 10:66–83.Google Scholar
Tufte, Edward R. 1983. The Visual Display of Quantitative Information, vol. 2 Cheshire, CT: Graphics Press.Google Scholar
Wang, Huiwen, Liu, Qiang, Mok, Henry M.K., Fu, Linghui, and Tse, Wai Man. 2007. ‘A Hyperspherical Transformation Forecasting Model for Compositional Data’. European Journal of Operational Research 179(2):459–468.Google Scholar
Ward, Michael D., and Gleditsch, Kristian Skrede. 2008. Spatial Regression Models. Quantitative Applications in the Social Sciences, vol. 155 New York: Sage Publications.Google Scholar
Williams, Laron K., and Whitten, Guy D.. 2011. ‘Dynamic Simulations of Autoregressive Relationships’. Stata Journal 11(4):577–588.Google Scholar
Williams, Laron K., and Whitten, Guy D.. 2012. ‘But Wait, Theres More! Maximizing Substantive Inferences From TSCS Models’. The Journal of Politics 74(3):685–693.Google Scholar
Supplementary material: PDF

Lipsmeyer et al supplementary material 1

Lipsmeyer et al supplementary material

Download Lipsmeyer et al supplementary material 1(PDF)
PDF 76.2 KB
Supplementary material: Link

Lipsmeyer Dataset

Dataset

http://dx.doi.org/10.7910/DVN/XHW4CB
Link