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Stochastic Process Methods with an Application to Budgetary Data

Published online by Cambridge University Press:  04 January 2017

Christian Breunig  and
Bryan D. Jones
Christian Breunig*
Affiliation:
Department of Political Science, University of Toronto, Sidney Smith Hall, Room 3018, 100 St. George Street, Toronto, Ontario M5S 3G3 Canada
Bryan D. Jones
Affiliation:
Department of Government, The University of Texas at Austin, 1 University Station A1800, Austin, TX 78712-0119. e-mail: bdjones@austin.utexas.edu
*
e-mail: c.breunig@utoronto.ca (corresponding author)
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Abstract

Political scientists have increasingly focused on causal processes that operate not solely on mean differences but on other stochastic characteristics of the distribution of a dependent variable. This paper surveys important statistical tools used to assess data in situations where the entire distribution of values is of interest. We first outline three broad conditions under which stochastic process methods are applicable and show that these conditions cover many domains of social inquiry. We discuss a variety of visual and analytical techniques, including distributional analysis, direct parameter estimates of probability density functions, and quantile regression. We illustrate the utility of these statistical tools with an application to budgetary data because strong theoretical expectations at the micro- and macrolevel exist about the distributional characteristics for such data. The expository analysis concentrates on three budget series (total, domestic, and defense outlays) of the U.S. government for 1800–2004.

Information

Type
Research Article
Information
Political Analysis , Volume 19 , Issue 1 , Winter 2011 , pp. 103 - 117
Copyright
Copyright © The Author 2010. Published by Oxford University Press on behalf of the Society for Political Methodology 

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Footnotes

Authors' Note: The authors would like to thank Michael D. Ward, John Ahlquist, and Samuel Workman, our reviewers, and the editors of Political Analysis for helpful comments. Supplementary materials for this article as well as R and Stata code for implementing the presented methods are available on the authors' Web site.

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