IDEAS home Printed from https://ideas.repec.org/a/cup/apsrev/v117y2023i1p59-79_5.html
   My bibliography  Save this article

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

Author

Listed:
  • COOK, SCOTT J.
  • HAYS, JUDE C.
  • FRANZESE, ROBERT J.

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.

Suggested Citation

  • Cook, Scott J. & Hays, Jude C. & Franzese, Robert J., 2023. "STADL Up! The Spatiotemporal Autoregressive Distributed Lag Model for TSCS Data Analysis," American Political Science Review, Cambridge University Press, vol. 117(1), pages 59-79, February.
    • Handle: RePEc:cup:apsrev:v:117:y:2023:i:1:p:59-79_5
    as

    Download full text from publisher

    File URL: https://www.cambridge.org/core/product/identifier/S0003055422000272/type/journal_article
    File Function: link to article abstract page
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Racek, Daniel & Thurner, Paul & Kauermann, Goeran, 2024. "Integrating Spatio-temporal Diffusion into Statistical Forecasting Models of Armed Conflict via Non-parametric Smoothing," OSF Preprints q59dr, Center for Open Science.
    2. Hafner-Burton, Emilie M & Schneider, Christina J, 2023. "The International Liberal Foundations of Democratic Backsliding," Institute on Global Conflict and Cooperation, Working Paper Series qt0965w1jb, Institute on Global Conflict and Cooperation, University of California.
    3. Tobias Ruttenauer, 2024. "Spatial Data Analysis," Papers 2402.09895, arXiv.org.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cup:apsrev:v:117:y:2023:i:1:p:59-79_5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Kirk Stebbing (email available below). General contact details of provider: https://www.cambridge.org/psr .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.