What To Do (and Not to Do) with Time-Series Cross-Section Data
1995/09/01 by Nathaniel Beck, Jonathan N. Katz · 6,753 citations
Economics, Econometrics and Finance · Mathematics · Psychology · #Computer science #Econometrics #Economics #Estimator #Field (mathematics) #Fiscal Policies and Political Economy #Fiscal Policy and Economic Growth #Mathematics #Monetary Policy and Economic Impact #Monte Carlo method #Overconfidence effect #Panel data #Psychology #Section (typography) #Series (stratigraphy) #Social psychology #Standard error #Statistics
paper · doi:10.2307/2082979
published in American Political Science Review 89(3), 634-647 (Cambridge University Press)
openalex publication_date 1995/09/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Abstract
We examine some issues in the estimation of time-series cross-section models, calling into question the conclusions of many published studies, particularly in the field of comparative political economy. We show that the generalized least squares approach of Parks produces standard errors that lead to extreme overconfidence, often underestimating variability by 50% or more. We also provide an alternative estimator of the standard errors that is correct when the error structures show complications found in this type of model. Monte Carlo analysis shows that these “panel-corrected standard errors” perform well. The utility of our approach is demonstrated via a reanalysis of one “social democratic corporatist” model.
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