A Bayesian approach for the estimation of weight matrices in spatial autoregressive models-Reference-Cited by-同舟云学术

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

Published:2022-07-22 Issue:1 Volume:18 Page:44-63
ISSN:1742-1772
Container-title:Spatial Economic Analysis
language:en
Short-container-title:Spatial Economic Analysis

Author:

Krisztin Tamás¹^ORCID,Piribauer Philipp²^ORCID

Affiliation:

1. International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria

2. Austrian Institute of Economic Research (WIFO), Vienna, Austria

Funder

Oesterreichische Nationalbank

Austrian National Bank)

Austrian Science Fund

Publisher

Informa UK Limited

Subject

Earth and Planetary Sciences (miscellaneous),Statistics, Probability and Uncertainty,General Economics, Econometrics and Finance,Geography, Planning and Development

Link

https://www.tandfonline.com/doi/pdf/10.1080/17421772.2022.2095426

Reference37 articles.

1. Two-Step Lasso Estimation of the Spatial Weights Matrix

2. Regional economic growth in Europe: A semiparametric spatial dependence approach

3. The effect of climate on the spread of the COVID-19 pandemic: A review of findings, and statistical and modelling techniques

4. Embracing heterogeneity: the spatial autoregressive mixture model

5. De Paula, Á., Rasul, I. & Souza, P. (2019). Identifying network ties from panel data: Theory and an application to tax competition. arXiv preprint arXiv:1910.07452.

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