Not Just for Cointegration: Error Correction Models with Stationary Data Luke Keele Department of Politics and International Relations Nuffield College and Oxford University Manor Road, Oxford OX1 3UQ UK Tele: +44 1865 278716 Email: luke.keele@politics.ox.ac.uk Suzanna De Boef Department of Political Science Pennsylvania State University State College, PA 16802 Tele: 814-863-9402 Email: sdeboef@psu.edu December 16, 2004 Abstract The error correction model is generally thought to be isomorphic to integrated data and the modeling of cointegrated processes, and as such, is considered in- appropriate for stationary data. Given that many political time series are not integrated, analysts are unable to take advantages of the error correction model’s ability to capture both long and short-term dynamics in a single statistical model. We use analytical results to demonstrate that error correction models are appropri- ate for stationary data. We use simulated data to then demonstrate the equivalency between auto-distributed lag models and error correction models. Finally, we re- estimate a model of Supreme Court approval from the literature to demonstrate how the use of an error correction model enhances our understanding of political dynamics. In 1993, Political Analysis published a series of articles designed to introduce coin- tegration and error correction models to the political science literature. This set of six articles is perhaps one of the best introductions to cointegration and error correction methods in print. But what is particularly interesting in this set of articles, besides the lucid introduction to cointegration, is the debate that develops among the authors on