Hostname: page-component-8448b6f56d-t5pn6 Total loading time: 0 Render date: 2024-04-18T22:55:25.361Z Has data issue: false hasContentIssue false

Time is Not A Theoretical Variable

Published online by Cambridge University Press:  04 January 2017

Nathaniel Beck*
Affiliation:
Department of Politics, New York University, 19 W. 4th St., 2nd Floor, New York, NY 10012. e-mail: nathaniel.beck@nyu.edu

Extract

Carter and Signorino (2010) (hereinafter “CS”) add another arrow, a simple cubic polynomial in time, to the quiver of the binary time series—cross-section data analyst; it is always good to have more arrows in one's quiver. Since comments are meant to be brief, I will discuss here only two important issues where I disagree: are cubic duration polynomials the best way to model duration dependence and whether we can substantively interpret duration dependence.

Type
Research Article
Copyright
Copyright © The Author 2010. Published by Oxford University Press on behalf of the Society for Political Methodology 

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.)

References

Beck, Nathaniel, and Jackman, Simon. 1998. Beyond linearity by default: Generalized additive models. American Journal of Political Science 42: 596627.Google Scholar
Beck, Nathaniel, and Jackman, Simon. 1999. Getting the mean right is a good thing: Generalized additive models. http://polmeth.wustl.edu/mediaDetail.php?docId=457.Google Scholar
Beck, Nathaniel, Katz, Jonathan, and Tucker, Richard. 1998. Taking time seriously: Time series cross section analysis with a binary dependent variable. American Journal of Political Science 42: 12601288.Google Scholar
Carter, David B., and Signorino, Curtis S. 2010. Back to the future: Modeling time dependence in binary data. Political Analysis 18: 271292.Google Scholar
Cox, David R. 1972. Regression models and life tables. Journal of the Royal Statistical Society, Series B 34: 187220.Google Scholar
Keele, Luke J. 2008. Semiparametric regression for the social sciences. New York: John Wiley & Sons.Google Scholar