Dealing with lags in longitudinal political data can be challenging when theory doesn't specify the lag length. This paper examines this common problem, reviewing traditional methods like distributed lag models and highlighting their limitations. A novel approach using Bayesian Model Averaging (BMA) is proposed to overcome these challenges by averaging over multiple possible lag structures simultaneously. The effectiveness of BMA is demonstrated through two compelling examples: analyzing a litigant signal model in American politics and testing modernization theory in political economy. These case studies show how atheoretic lags complicate standard analysis even as datasets grow more complex, proving that this method provides clearer insights than conventional alternatives for time-series cross-sectional research.

Key Concepts & Methods

* Distributed Lag Models: Standard approach when theoretical lag length is unknown.

* Bayesian Model Averaging (BMA): Novel alternative technique averaging over multiple plausible lag structures simultaneously.

* Time-Series Cross-Sectional Data: Complex datasets tracking changes across time and different political units.

Why It Matters

This approach addresses the growing complexity in analyzing longitudinal data without clear theoretical guidance, offering robust solutions for researchers facing atheoretic lags.