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

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...

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Bibliographic Details
Published inThe American political science review Vol. 117; no. 1; pp. 59 - 79
Main Authors COOK, SCOTT J., HAYS, JUDE C., FRANZESE, ROBERT J.
Format Journal Article
LanguageEnglish
Published New York, USA Cambridge University Press 01.02.2023
Subjects
Automation
Bias
Clustering
Data analysis
Democracy
Diagnostic Tests
Economic development
Effects
Inferences
Matrices
Political science
Researchers
Specification
Time series
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Summary: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.
ISSN:0003-0554
1537-5943
DOI:10.1017/S0003055422000272