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A Bayesian approach for estimation of weight matrices in spatial autoregressive models

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  • Tam'as Krisztin
  • Philipp Piribauer

Abstract

We develop a Bayesian approach to estimate weight matrices in spatial autoregressive (or spatial lag) models. Datasets in regional economic literature are typically characterized by a limited number of time periods T relative to spatial units N. When the spatial weight matrix is subject to estimation severe problems of over-parametrization are likely. To make estimation feasible, our approach focusses on spatial weight matrices which are binary prior to row-standardization. We discuss the use of hierarchical priors which impose sparsity in the spatial weight matrix. Monte Carlo simulations show that these priors perform very well where the number of unknown parameters is large relative to the observations. The virtues of our approach are demonstrated using global data from the early phase of the COVID-19 pandemic.

Suggested Citation

  • Tam'as Krisztin & Philipp Piribauer, 2021. "A Bayesian approach for estimation of weight matrices in spatial autoregressive models," Papers 2101.11938, arXiv.org, revised Aug 2022.
    • Handle: RePEc:arx:papers:2101.11938
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    Cited by:

    1. Christian Glocker & Matteo Iacopini & Tam'as Krisztin & Philipp Piribauer, 2023. "A Bayesian Markov-switching SAR model for time-varying cross-price spillovers," Papers 2310.19557, arXiv.org.
    2. Piribauer, Philipp & Glocker, Christian & Krisztin, Tamás, 2023. "Beyond distance: The spatial relationships of European regional economic growth," Journal of Economic Dynamics and Control, Elsevier, vol. 155(C).
    3. Nikolas Kuschnig, 2021. "Bayesian Spatial Econometrics and the Need for Software," Department of Economics Working Papers wuwp318, Vienna University of Economics and Business, Department of Economics.
    4. Tamás Krisztin & Philipp Piribauer, 2023. "A joint spatial econometric model for regional FDI and output growth," Papers in Regional Science, Wiley Blackwell, vol. 102(1), pages 87-106, February.
    5. Nikolas Kuschnig, 2022. "Bayesian spatial econometrics: a software architecture," Journal of Spatial Econometrics, Springer, vol. 3(1), pages 1-25, December.
    6. Deborah Gefang & Stephen G. Hall & George S. Tavlas, 2023. "Identifying spatial interdependence in panel data with large N and small T," Papers 2309.03740, arXiv.org.

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