Tamás Krisztin, Philipp Piribauer
arXiv 28 Jan 2021 · Econometrics · publishedSpatial Economic Analysis (2022) · 2 citations (OpenAlex)
arXiv:2101.11938 · PDF · DOI · OpenAlex · Extracted main text
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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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | LeSage JP and Pace RK (2009) Introduction to Spatial Econometrics CRC Press, Boca Raton London New York | 1.000 | 10 | 4 | 100% |
| 2 | Piribauer P and Cuaresma JC (2016) Bayesian variable selection in sp… Spatial Economic Analysis 11(4), 457–479 | 0.737 | 3 | 2 | 100% |
| 3 | Vega HS and Elhorst JP (2015) The SLX model Journal of Regional Science 55(3), 339–363 | 0.644 | 2 | 2 | 100% |
| 4 | Debarsy N and LeSage J (2018) Flexible dependence modeling using con… Regional Science and Urban Economics 69, 48–68 | 0.644 | 2 | 2 | 100% |
| 5 | De Paula Á, Rasul I and Souza P (2019) Identifying network ties from… arXiv preprint arXiv:1910.07452 | 0.585 | 3 | 1 | 100% |
| 6 | Krisztin T, Piribauer P and Wögerer M (2020) The spatial econometric… Letters in Spatial and Resource Sciences 13, 209–218 | 0.585 | 3 | 1 | 100% |
| 7 | Ahrens A and Bhattacharjee A (2015) Two-step lasso estimation of the… Econometrics 3(1), 128–155 | 0.511 | 2 | 1 | 100% |
| 8 | Guliyev H (2020) Determining the spatial effects of COVID-19 using t… Spatial Statistics 38, 100443 | 0.511 | 2 | 1 | 100% |
| 9 | Han X, Xu Y, Fan L, Huang Y, Xu M and Gao S (2021) Quantifying COVID… Proceedings of the National Academy of Sciences 118(31) | 0.511 | 2 | 1 | 100% |
| 10 | Basile R (2008) Regional economic growth in Europe: A semiparametric… Papers in Regional Science 87(4), 527–544 | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 37 scored citations.
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| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | A Bayesian Markov-switching SAR model for time-varying cross-price spillovers | 1.000 | 5 | 3 |
| 2 | Estimation and Inference for Latent Dual Networks Using High-Dimensional IV Screening | 0.405 | 1 | 1 |