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

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Tamás Krisztin, Philipp Piribauer

Econometrics · Submitted 28 Jan 2021

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Monte Carlo simulations show that these priors perform very well where the number of unknown parameters is large relative to the observations.

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arXiv ID2101.11938 (version 2)
Pith IDRD6U3BJI, Pith's permanent record of this paper and its review
CitationPhilipp Piribauer, Tam\'as Krisztin. "A Bayesian approach for estimation of weight matrices in spatial autoregressive models." arXiv:2101.11938 (2021). https://pith.science/paper/RD6U3BJI
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arXiv listing abstract v2. Results and the main claim are underlined; .

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.

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What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 We develop a Bayesian approach to estimate weight matrices in spatial autoregressive (or spatial lag) models. contribution 1.00 0.95 0.15 asserted 1.00
s1 Datasets in regional economic literature are typically characterized by a limited number of time periods T relative to spatial units N. background 1.00 0.07 0.02 asserted 0.99
s2 When the spatial weight matrix is subject to estimation severe problems of over-parametrization are likely. background 0.96 0.11 0.05 hedged possibility 0.97
s3 To make estimation feasible, our approach focusses on spatial weight matrices which are binary prior to row-standardization. method 0.96 0.76 0.07 asserted 0.99
s4 We discuss the use of hierarchical priors which impose sparsity in the spatial weight matrix. method 0.96 0.77 0.07 asserted 0.87
s5 Monte Carlo simulations show that these priors perform very well where the number of unknown parameters is large relative to the observations. result 1.00 0.94 0.24 asserted 1.00
s6 The virtues of our approach are demonstrated using global data from the early phase of the COVID-19 pandemic. contribution 0.37 0.89 0.17 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 0.79.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

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Original 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.

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