2010/01/01 by David B. Carter, Curtis S. Signorino · 31 citations
Economics, Econometrics and Finance · Social Sciences · #Defense, Military, and Policy Studies #Monetary Policy and Economic Impact #Qualitative Comparative Analysis Research
paper · pdf · doi:10.1093/pan/mpq013
openalex publication_date 2010/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31
Since Beck, Katz, and Tucker (1998), the standard method for modeling time dependence in binary data has been to incorporate time dummies or splined time in logistic regressions. Although we agree with the need for modeling time dependence, we demonstrate that time dummies can induce estimation problems due to separation. Splines do not suffer from these problems. However, the complexity of splines has led substantive researchers (1) to use knot values that may be inappropriate for their data and (2) to ignore any substantive discussion concerning temporal dependence. We propose a relatively simple alternative: including t, t 2 , and t 3 in the regression. This cubic polynomial approximation is trivial to implement—and, therefore, interpret—and it avoids problems such as quasi-complete separation. Monte Carlo analysis demonstrates that, for the types of hazards one often sees in substantive research, the polynomial approximation always outperforms time dummies and generally performs as well as splines or even more flexible autosmoothing procedures. Due to its simplicity, this method also accommodates nonproportional hazards in a straightforward way. We reanalyze Crowley and Skocpol (2001) using nonproportional hazards and find new empirical support for the historical-institutionalist perspective.