Author:
Wilson Sven E.,Butler Daniel M.
Abstract
In 1995, Beck and Katz (B&K) instructed the profession on “What to do (and not to do) with time-series, cross-section data,” and almost instantly their prescriptions became the new orthodoxy for practitioners. Our assessment of the intellectual aftermath of this paper, however, does not inspire confidence in the conclusions reached during the past decade. The 195 papers we reviewed show a widespread failure to diagnose and treat common problems of time-series, cross-section (TSCS) data analysis. To show the importance of the consequences of the B&K assumptions, we replicate eight papers in prominent journals and find that simple alternative specifications often lead to drastically different conclusions. Finally, we summarize many of the statistical issues relative to TSCS data and show that there is a lot more to do with TSCS data than many researchers have apparently assumed.
Publisher
Cambridge University Press (CUP)
Subject
Political Science and International Relations,Sociology and Political Science
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5. We note here that a different type of problem can occur if the data are not measured frequently enough, namely that of temporal aggregation, which can lead to several types of incorrect inferences. If the true generating process is such that observations occur at frequent intervals, then the empirical model should include observations measured at the same frequency. Really, temporal aggregation is the same type of problem we are discussing here, but in reverse—namely, masking legitimate observations with temporal aggregates. Temporal aggregation has long been studied in the econometric literature but has been mostly neglected in political science. Important exceptions include Freeman (1989), Alt, King, and Signorino (2001), and Shellman (2004).
Cited by
268 articles.
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