STADL Up! The Spatiotemporal Autoregressive Distributed Lag Model for TSCS Data Analysis Academic Article uri icon

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 dimensions 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 implementationfrom automation of common spatial-weights matrices (W) through estimated spatiotemporal effects/response calculationsfor their own TSCS analyses.

published proceedings

  • American Political Science Review

author list (cited authors)

  • COOK, S. J., HAYS, J. C., & FRANZESE, R. J.

complete list of authors

  • COOK, SCOTT J||HAYS, JUDE C||FRANZESE, ROBERT J

publication date

  • January 1, 2022 11:11 AM