Description: “More than any other journal, Political Analysis publishes the most powerful new ideas for the empirical analysis of politics. No serious researcher in the field, and no serious research library, will want to be without a subscription.”
Prof. Christopher Achen, Department of Political Science, University of Michigan
“Political Analysis is the leading journal for innovative new quantitative work in political methodology, and the journal shows great promise for becoming an important voice in the ongoing effort to bridge quantitative and qualitative methodology.'
Prof. David Collier, Department of Political Science, University of California, Berkeley
Coverage: 1989-2014 (Vol. 1 - Vol. 22, No. 4)
The "moving wall" represents the time period between the last issue available in JSTOR and the most recently published issue of a journal. Moving walls are generally represented in years. In rare instances, a publisher has elected to have a "zero" moving wall, so their current issues are available in JSTOR shortly after publication.
Note: In calculating the moving wall, the current year is not counted.
For example, if the current year is 2008 and a journal has a 5 year moving wall, articles from the year 2002 are available.
- Terms Related to the Moving Wall
- Fixed walls: Journals with no new volumes being added to the archive.
- Absorbed: Journals that are combined with another title.
- Complete: Journals that are no longer published or that have been combined with another title.
Subjects: Political Science, Social Sciences
Collections: Arts & Sciences IX Collection
For political scientists who engage in longitudinal analyses, the question of how best to deal with nonstationary time-series is anything but settled. While many believe that little is lost when the focus of empirical models shifts from the nonstationary levels to the stationary changes of a series, others argue that such an approach erases any evidence of a long-term relationship among the variables of interest. But the pitfalls of working directly with integrated series are well known, and post-hoc corrections for serially correlated errors often seem inadequate. Compounding (or perhaps alleviating, if one believes in the power of selective perception) the difficult question of whether to difference a time-series is the fact that analysts have been forced to rely on subjective diagnoses of the stationarity of their data. Thus, even if one felt strongly about the superiority of one modeling approach over another, the procedure for determining whether that approach is even applicable can be frustrating.