STADL Up! The Spatiotemporal Autoregressive Distributed Lag Model for TSCS Data Analysis

Scott J. Cook, Jude C. Hays and Robert J. Franzese

American Political Science Review, 2023, vol. 117, issue 1, 59-79

Abstract: Time-series cross-section (TSCS) data are prevalent in political science, yet many distinct challenges presented by TSCS data remain underaddressed. We focus on how dependence in both space and time complicates estimating either spatial or temporal dependence, dynamics, and effects. Little is known about how modeling one of temporal or cross-sectional dependence well while neglecting the other affects results in TSCS analysis. We demonstrate analytically and through simulations how misspecification of either temporal or spatial dependence inflates estimates of the other dimension’s dependence and thereby induces biased estimates and tests of other covariate effects. Therefore, we recommend the spatiotemporal autoregressive distributed lag (STADL) model with distributed lags in both space and time as an effective general starting point for TSCS model specification. We illustrate with two example reanalyses and provide R code to facilitate researchers’ implementation—from automation of common spatial-weights matrices (W) through estimated spatiotemporal effects/response calculations—for their own TSCS analyses.

Date: 2023
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