Raising the bar in spatial economic analysis: two laws of spatial economic modelling

REFERENCES

• Aidt, T., & Leon-Ablan, G. (2022). The interaction of structural factors and diffusion in social unrest: Evidence from the Swing Riots. British Journal of Political Science, 52(2), 869–885. https://doi.org/10.1017/S0007123420000873
   Web of Science ®Google Scholar
• Aidt, T., Leon-Ablan, G., & Satchell, M. (2021). The social dynamics of collective action: Evidence from the diffusion of the Swing Riots, 1830-1831. The Journal of Politics, 84(1), 209–225. https://doi.org/10.1086/714784
   Web of Science ®Google Scholar
• Alvarado-Quesada, I., & Weikard, H.-P. (2017). International cooperation on biodiversity conservation when spatial structures matter. Spatial Economic Analysis, 12(1), 27–49. https://doi.org/10.1080/17421772.2017.1259494
   Web of Science ®Google Scholar
• Anselin, L., & Lozano-Gracia, N. (2009). Spatial hedonic models. In T. C. Mills, & K. Patterson (Eds.), Palgrave handbook of econometrics, volume 2, applied econometrics (pp. 1213–1250). Palgrave Macmillan.
   Google Scholar
• Balazsi, L., Matyas, L., & Wansbeek, T. (2018). The estimation of multidimensional fixed effects panel data models. Econometric Reviews, 37(3), 212–227. https://doi.org/10.1080/07474938.2015.1032164
   Web of Science ®Google Scholar
• Balázsi, L., Mátyás, L., & Wansbeek, T. (2024). Fixed effects models. In L. Balázsi (Ed.), The econometrics of multi-dimensional panels (2nd ed., pp. 1–37). Springer. https://doi.org/10.1007/978-3-031-49849-7_1.
   Google Scholar
• Balia, S., Brau, R., & Marrocu, E. (2018). Interregional patient mobility in a decentralized healthcare system. Regional Studies, 52(3), 388–402. https://doi.org/10.1080/00343404.2017.1307954
   Web of Science ®Google Scholar
• Bao, Y., & Liu, X. (2021). Estimating a spatial autoregressive model with autoregressive disturbances based on the indirect inference principle. Spatial Economic Analysis, 16(4), 506–529. https://doi.org/10.1080/17421772.2021.1902552
   Web of Science ®Google Scholar
• Batabyal, A. A., & Folmer, H. (2020). Spatial economic aspects of climate change. Spatial Economic Analysis, 15(3), 209–218. https://doi.org/10.1080/17421772.2020.1788221
   Web of Science ®Google Scholar
• Bayer, P., Ferreira, F., & McMillan, R. (2004). Tiebout sorting, social multipliers and the demand for school quality. NBER No. 10871.
   Google Scholar
• Beenstock, M., & Felsenstein, D. (2019). The econometric analysis of non-stationary spatial panel data. Springer. https://doi.org/10.1007/978-3-030-03614-0.
   Google Scholar
• Beenstock, M., Felsenstein, D., & Xieer, D. (2018). Spatial econometric analysis of spatial general equilibrium. Spatial Economic Analysis, 13(3), 356–378. https://doi.org/10.1080/17421772.2018.1403645
   Web of Science ®Google Scholar
• Bhattacharjee, A., Castro, E., Maiti, T., & Marques, J. (2016). Endogenous spatial regression and delineation of submarkets: A new framework with application to housing markets. Journal of Applied Econometrics, 31(1), 32–57. https://doi.org/10.1002/jae.2478
   Web of Science ®Google Scholar
• Billé, A. G., Benedetti, R., & Postiglione, P. (2017). A two-step approach to account for unobserved spatial heterogeneity. Spatial Economic Analysis, 12(4), 452–471. https://doi.org/10.1080/17421772.2017.1286373
   Web of Science ®Google Scholar
• Blanchflower, D. G., & Oswald, A. J. (1994). The wage curve. MIT Press.
   Google Scholar
• Bond-Smith, S., McCann, P., & Oxley, L. (2018). A regional model of endogenous growth without scale assumptions. Spatial Economic Analysis, 13(1), 5–35. https://doi.org/10.1080/17421772.2018.1392038
   Web of Science ®Google Scholar
• Brakman, S., Garretsen, H., & Marrewijk, C. v. (2009). The new introduction to geographical economics. Cambridge University Press.
   Google Scholar
• Breitung, J., & Wigger, C. (2018). Alternative GMM estimators for spatial regression models. Spatial Economic Analysis, 13(2), 148–170. https://doi.org/10.1080/17421772.2018.1403644
   Web of Science ®Google Scholar
• Bruna, F., Lopez-Rodriguez, J., & Faíña, A. (2016). Market potential, spatial dependences and spillovers in European regions. Regional Studies, 50(9), 1551–1563. https://doi.org/10.1080/00343404.2015.1048796
   Web of Science ®Google Scholar
• Capello, R., Caragliu, A., & Dellisanti, R. (2023). Integrating digital and global transformations in forecasting regional growth: The MASST5 model. Spatial Economic Analysis, 19(2), 1–28. https://doi.org/10.1080/17421772.2023.2278514
   Google Scholar
• Carrion-i-Silvestre, J. L., & Surdeanu, L. (2016). Productivity, infrastructure and human capital in the Spanish regions. Spatial Economic Analysis, 11(4), 365–391. https://doi.org/10.1080/17421772.2016.1189089
   Web of Science ®Google Scholar
• Cazals, C., Florens, J.-P., & Simar, L. (2002). Nonparametric frontier estimation: A robust approach. Journal of Econometrics, 106(1), 1–25. https://doi.org/10.1016/S0304-4076(01)00080-X
   Web of Science ®Google Scholar
• Chudik, A., Pesaran, M., & Tosetti, E. (2011). Weak and strong cross-section dependence and estimation of large panels. Econometrics Journal, 14(1), 45–90. https://doi.org/10.1111/j.1368-423X.2010.00330.x
   Web of Science ®Google Scholar
• Chudik, A., & Pesaran, M. H. (2016). Theory and practice of GVAR modelling. Journal of Economic Surveys, 30(1), 165–197. https://doi.org/10.1111/joes.12095
   Web of Science ®Google Scholar
• Colwell, P. F., Cannaday, R. E., & Wu, C. (1983). The analytical foundations of adjustment grid methods. Journal of the American Real Estate and Urban Economics Association, 11(1), 11–29. https://doi.org/10.1111/1540-6229.00277
   Google Scholar
• Conigliani, C., Costantini, V., & Finardi, G. (2022). Climate-related natural disasters and forced migration: A spatial regression analysis. Spatial Economic Analysis, 17(3), 416–439. https://doi.org/10.1080/17421772.2021.1995620
   Web of Science ®Google Scholar
• Conley, T. G., Hansen, C. B., & Rossi, P. E. (2012). Plausibly exogenous. The Review of Economics and Statistics, 94(1), 260–272. https://www.jstor.org/stable/41349174. https://doi.org/10.1162/REST_a_00139
   Web of Science ®Google Scholar
• Corrado, L., & Fingleton, B. (2012). Where is the economics in spatial econometrics? Journal of Regional Science, 52(2), 210–239. https://doi.org/10.1111/j.1467-9787.2011.00726.x
   Web of Science ®Google Scholar
• Credit, K. (2019). Transitive properties: A spatial econometric analysis of new business creation around transit. Spatial Economic Analysis, 14(1), 26–52. https://doi.org/10.1080/17421772.2019.1523548
   Web of Science ®Google Scholar
• Debarsy, N., & LeSage, J. P. (2022). Bayesian model averaging for spatial autoregressive models based on convex combinations of different types of connectivity matrices. Journal of Business & Economic Statistics, 40(2), 547–558. https://doi.org/10.1080/07350015.2020.1840993
   Web of Science ®Google Scholar
• Delgado, F. J., Lago-Peñas, S., & Mayor, M. (2018). Local tax interaction and endogenous spatial weights based on quality of life. Spatial Economic Analysis, 13(3), 296–318. https://doi.org/10.1080/17421772.2018.1420213
   Web of Science ®Google Scholar
• Dubé, J., Legros, D., Thériault, M., & Des Rosiers, F. (2017). Measuring and interpreting urban externalities in real-estate data: A spatio-temporal difference-in-differences (STDID) estimator. Buildings, 7(4), 51. https://doi.org/10.3390/buildings7020051
   Google Scholar
• Elhorst, J. P. (2010). Applied spatial econometrics: Raising the bar. Spatial Economic Analysis, 5(1), 9–28. https://doi.org/10.1080/17421770903541772
   Web of Science ®Google Scholar
• Elhorst, J. P. (2014). Spatial econometrics: From cross-sectional data to spatial panels. Springer.
   Google Scholar
• Elhorst, J. P., & Emili, S. (2022). A spatial econometric multivariate model of Okun’s law. Regional Science and Urban Economics, 93, 103756. https://doi.org/10.1016/j.regsciurbeco.2021.103756
   Web of Science ®Google Scholar
• Elhorst, J. P., Gross, M., & Tereanu, E. (2021). Cross-sectional dependence and spillovers in space and time: Where spatial econometrics and Global VAR models meet. Journal of Economic Surveys, 35(1), 192–226. https://doi.org/10.1111/joes.12391
   Web of Science ®Google Scholar
• Epifani, I., Ghiringhelli, C., & Nicolini, R. (2020). Population distribution over time: Modelling local spatial dependence with a CAR process. Spatial Economic Analysis, 15(2), 120–144. https://doi.org/10.1080/17421772.2020.1708442
   Web of Science ®Google Scholar
• Fingleton, B., & Palombi, S. (2015). Bootstrap J test for panel data models with spatially dependent error components, a spatial lag and additional endogenous variables. Spatial Economics Analysis, 11(1), 7–26. https://doi.org/10.1080/17421772.2016.1102960
   Web of Science ®Google Scholar
• Firmino Costa da Silva, D., Elhorst, J. P., & Neto Silveira, R. d. M. (2017). Urban and rural population growth in a spatial panel of municipalities. Regional Studies, 51(6), 894–908. https://doi.org/10.1080/00343404.2016.1144922
   Web of Science ®Google Scholar
• Fogli, A., & Veldkamp, L. (2011). Nature or nurture? Learning and the geography of female labor force paraticipation. Econometrica, 79(4), 1103–1138. https://doi.org/10.3982/ECTA7767
   Web of Science ®Google Scholar
• Forslid, R., & Okubo, T. (2021). Agglomeration of low-productive entrepreneurs to large regions: A simple model. Spatial Economic Analysis, 16(4), 471–486. https://doi.org/10.1080/17421772.2021.1884280
   Web of Science ®Google Scholar
• Fotheringham, A. S., Brunsdon, C., & Charlton, M. E. (2002). Geographically weighted regression: The analysis of spatially varying relationships. Wiley.
   Google Scholar
• Fotheringham, A. S., Oshan, T. M., & Li, Z. (2024). Multiscale geographically weighted regression: Theory and practice. CRC Press.
   Google Scholar
• Fujimoto, T. (2018). Appropriate assumption on cross-hauling national input–output table regionalization. Spatial Economic Analysis, 14(1), 106–128. https://doi.org/10.1080/17421772.2018.1506151
   Web of Science ®Google Scholar
• Fujita, M., Krugman, P., & Venables, A. J. (1999). The spatial economy; cities, regions, and international trade. MIT Press.
   Google Scholar
• Gross, M. (2018). Estimating GVAR weight matrices. Spatial Economic Analysis, 14(2), 219–240. https://doi.org/10.1080/17421772.2019.1556800
   Web of Science ®Google Scholar
• Haapanen, M., & Böckerman, P. (2016). More educated, more mobile? Evidence from post-secondary education reform. Spatial Economic Analysis, 12(1), 8–26. https://doi.org/10.1080/17421772.2017.1244610
   Web of Science ®Google Scholar
• Haddad, E. A., Perobelli, F. S., Araújo, I. F., & Bugarin, K. S. S. (2020). Structural propagation of pandemic shocks: An input–output analysis of the economic costs of COVID-19. Spatial Economic Analysis, 16(3), 252–270. https://doi.org/10.1080/17421772.2020.1844284
   Web of Science ®Google Scholar
• Halleck Vega, S., & Elhorst, J. P. (2016). A regional unemployment model simultaneously accounting for serial dynamics, spatial dependence and common factors. Regional Science and Urban Economics, 60, 85–95. https://doi.org/10.1016/j.regsciurbeco.2016.07.002
   Web of Science ®Google Scholar
• Hanson, G. H. (2005). Market potential, increasing returns and geographic concentration. Journal of International Economics, 67(1), 1–24. https://doi.org/10.1016/j.jinteco.2004.09.008
   Web of Science ®Google Scholar
• Harris, C. (1954). The market as a factor in the localization of industry in the United States. Annals of the Association of American Geographers, 44(4), 315–348. https://doi.org/10.2307/2561395.
   Web of Science ®Google Scholar
• Head, K., & Mayer, T. (2014). Gravity equations: Workhorse, toolkit, and cookbook. In G. Gopinath, E. Helpman, & K. Rogoff (Eds.), Handbook of international economics (Vol. 4, pp. 131–195). Elsevier. https://doi.org/10.1016/B978-0-444-54314-1.00003-3.
   Google Scholar
• Hill, R. J. (2013). Hedonic price indexes for residential housing: A survey, evaluation and taxonomy. Journal of Economic Surveys, 27(5), 879–914. https://doi.org/10.1111/j.1467-6419.2012.00731.x
   Web of Science ®Google Scholar
• Huang, G., Huang, H.-C., Liu, X., & Zhang, J. (2020). Endogeneity in Okun’s law. Applied Economics Letters, 27(11), 910–914. https://doi.org/10.1080/13504851.2019.1646861
   Web of Science ®Google Scholar
• Ibragimov, M., & Ibragimov, R. (2017). Unemployment and output dynamics in CIS countries: Okun’s law revisited. Applied Economics, 49(34), 3453–3479. https://doi.org/10.1080/00036846.2016.1262519
   Web of Science ®Google Scholar
• Isakson, H. R. (2002). The linear algebra of the sales comparison approach. The Journal of Real Estate Research, 24(2), 117–128. https://doi.org/10.1080/10835547.2002.12091090
   Google Scholar
• Jahn, M. (2017). Extending the FLQ formula: A location quotient-based interregional input-output framework. Regional Studies, 51(10), 1518–1529. https://doi.org/10.1080/00343404.2016.1198471
   Web of Science ®Google Scholar
• Jahn, M., Flegg, A. T., & Tohmo, T. (2020). Testing and implementing a new approach to estimating interregional output multipliers using input–output data for South Korean regions. Spatial Economic Analysis, 15(2), 165–185. https://doi.org/10.1080/17421772.2020.1720918
   Web of Science ®Google Scholar
• Kato, H. (2018). Lobbying and tax competition in an oligopolistic industry: A reverse home-market effect. Spatial Economic Analysis, 13(3), 276–295. https://doi.org/10.1080/17421772.2018.1425477
   Web of Science ®Google Scholar
• Kelejian, H. H., & Piras, G. (2014). Estimation of spatial models with endogenous weighting matrices, and an application to a demand model for cigarettes. Regional Science and Urban Economics, 46, 140–149. https://doi.org/10.1016/j.regsciurbeco.2014.03.001
   Web of Science ®Google Scholar
• Kelejian, H. H., & Piras, G. (2017). Spatial econometrics. Elsevier, Academic Press.
   Google Scholar
• Kelejian, H. H., & Prucha, I. R. (2010). Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances. Journal of Econometrics, 157(1), 53–67. https://doi.org/10.1016/j.jeconom.2009.10.025
   PubMed Web of Science ®Google Scholar
• Krisztin, T., & Piribauer, P. (2022). A Bayesian approach for the estimation of weight matrices in spatial autoregressive models. Spatial Economic Analysis, 18(1), 44–63. https://doi.org/10.1080/17421772.2022.2095426
   Web of Science ®Google Scholar
• Kuersteiner, G. M., & Prucha, I. R. (2020). Dynamic spatial panel models: Networks, common shocks, and sequential exogeneity. Econometrica, 88(5), 2109–2146. https://doi.org/10.3982/ECTA13660
   Web of Science ®Google Scholar
• Lamonica, G. R., Recchioni, M. C., Chelli, F. M., & Salvati, L. (2019). The efficiency of the cross-entropy method when estimating the technical coefficients of input–output tables. Spatial Economic Analysis, 15(1), 62–91. https://doi.org/10.1080/17421772.2019.1615634
   Web of Science ®Google Scholar
• Laurent, T., Margaretic, P., & Thomas-Agnan, C. (2022). Neighbouring countries and bilateral remittances: A global study. Spatial Economic Analysis, 17(4), 557–584. https://doi.org/10.1080/17421772.2022.2070656
   Web of Science ®Google Scholar
• Lee, J., & Huang, Y. (2022). COVID-19 impact on US housing markets: Evidence from spatial regression models. Spatial Economic Analysis, 17(3), 395–415. https://doi.org/10.1080/17421772.2021.2018028
   Web of Science ®Google Scholar
• Lee, L.-F. (2024). Spatial econometrics: Spatial autoregressive models. World Scientific.
   Google Scholar
• LeGallo, J., López, F. A., & Chasco, C. (2020). Testing for spatial group-wise heteroskedasticity in spatial autocorrelation regression models: Lagrange multiplier scan tests. Annals of Regional Science, 64(2), 287–312. https://doi.org/10.1007/s00168-019-00919-w
   Web of Science ®Google Scholar
• LeSage, J. P. (2014). Spatial econometric panel data model specification: A Bayesian approach. Spatial Statistics, 9, 122–145. https://doi.org/10.1016/j.spasta.2014.02.002
   Web of Science ®Google Scholar
• LeSage, J. P. (2015). Software for Bayesian cross section and panel spatial model comparison. Journal of Geographical Systems, 17(4), 297–310. https://doi.org/10.1007/s10109-015-0217-3
   Web of Science ®Google Scholar
• LeSage, J. P., & Pace, R. K. (2009). Introduction to spatial econometrics. CRC Press.
   Google Scholar
• LeSage, J. P., & Parent, O. (2007). Bayesian model averaging for spatial econometric models. Geographical Analysis, 39(3), 241–267. https://doi.org/10.1111/j.1538-4632.2007.00703.x
   Web of Science ®Google Scholar
• Lewbel, A., Qu, L., & Tang, X. (2023). Social networks with unobserved links. Journal of Political Economy, 131(4), 898–946. https://doi.org/10.1086/722090
   Google Scholar
• Li, M., Lenzen, M., Pedauga, L. E., & Malik, A. (2022). A minimum-disruption approach to input–output disaster analysis. Spatial Economic Analysis, 17(4), 446–470. https://doi.org/10.1080/17421772.2022.2056231
   Web of Science ®Google Scholar
• Lisi, G. (2019). Sales comparison approach, multiple regression analysis and the implicit prices of housing. Journal of Property Research, 36(3), 272–290. https://doi.org/10.1080/09599916.2019.1651755
   Web of Science ®Google Scholar
• Matyas, L. (2017). The econometrics of multi-dimensional panels: Theory and applications. Springer. https://doi.org/10.1007/978-3-319-60783-2.
   Google Scholar
• Meng, M., Lee, J., & Payne, J. E. (2017). RALS-LM unit root test with trend breaks and non-normal errors: Application to the Prebisch–Singer hypothesis. Studies in Nonlinear Dynamics & Econometrics, 21(1), 31–45. https://doi.org/10.1515/snde-2016-0050
   Web of Science ®Google Scholar
• Miller, R. E., & Blair, P. D. (2022). Input-output analysis: Foundations and extensions (3rd ed). Cambridge University Press. https://doi.org/10.1017/9781108676212.
   Google Scholar
• Morrissey, K. (2020). Big data and its potential role in regional growth: Evidence from Great Britain. Spatial Economic Analysis, 15(4), 494–504. https://doi.org/10.1080/17421772.2020.1825783
   Web of Science ®Google Scholar
• Neymayer, E., & Plümper, T. (2016). W. Political Science Research and Methods, 4(1), 175–193. https://doi.org/10.1017/psrm.2014.40
   Google Scholar
• Noblet, S., & Belgodere, A. (2016). Coordination costs and the geography of production. Spatial Economic Analysis, 11(4), 392–412. https://doi.org/10.1080/17421772.2016.1189088
   Web of Science ®Google Scholar
• O’Kelly, M. (2016). Global airline networks: Comparative nodal access measures. Spatial Economic Analysis, 11(3), 253–275. https://doi.org/10.1080/17421772.2016.1177262
   Web of Science ®Google Scholar
• Okun, A. M. (1962). Potential GNP: Its measurement and significance. American Statistical Association. Proceedings of the Business and Economic Statistics Section, 98–104.
   Google Scholar
• Oosterhaven, J. (2022). Rethinking input-output analysis: A spatial perspective (2nd revised and extended edition). Springer. https://doi.org/10.1007/978-3-031-05087-9.
   Google Scholar
• Oosterhaven, J., & Többen, J. (2017). Wider economic impacts of heavy flooding in Germany: A non-linear programming approach. Spatial Economic Analysis, 12(4), 404–428. https://doi.org/10.1080/17421772.2017.1300680
   Web of Science ®Google Scholar
• Op’t Veld, D., Bijlsma, E., & Hoef, P. v. d. (2008). Automated valuation in the Dutch housing market: The web-application ‘marktpositie’ used by NVM-realtors. In T. Kauko, & M. d’Amato (Eds.), Advances in mass appraisal methods (pp. 70–90). Blackwell Publishing.
   Google Scholar
• Pace, R. K., Barry, R., Clapp, J. M., & Rodriquez, M. (1998). Spatiotemporal autoregressive models of neighborhood effects. Journal of Real Estate Finance and Economics, 17(1), 15–33. https://doi.org/10.1023/A:1007799028599
   Web of Science ®Google Scholar
• Pakizeh, A. H., & Kashani, H. (2021). Application of machine-learning models to estimate regional input coefficients and multipliers. Spatial Economic Analysis, 17(2), 178–205. https://doi.org/10.1080/17421772.2021.1959046
   Web of Science ®Google Scholar
• Panzera, D., Benedetti, R., & Postiglioni, P. (2016). A Bayesian approach to parameter estimation in the presence of spatial missing data. Spatial Economic Analysis, 11(2), 201–218. https://doi.org/10.1080/17421772.2016.1102962
   Web of Science ®Google Scholar
• Partridge, M. D., Boarnet, M., Brakman, S., & Ottaviano, G. (2012). Introduction: Whither spatial econometrics? Journal of Regional Science, 52(2), 167–171. https://doi.org/10.1111/j.1467-9787.2012.00767.x
   Web of Science ®Google Scholar
• Patuelli, R., & Arbia, G. (2016). Spatial econometric interaction modelling. Springer. https://doi.org/10.1007/978-3-319-30196-9.
   Google Scholar
• Pereira-López, X., Carrascal-Incera, A., & Fernández-Fernández, M. (2020). A bidimensional reformulation of location quotients for generating input–output tables. Spatial Economic Analysis, 15(4), 476–493. https://doi.org/10.1080/17421772.2020.1729996
   Web of Science ®Google Scholar
• Pesaran, M. H. (2015). Time series and panel data econometrics. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780198736912.001.0001.
   Google Scholar
• Piacentino, D., Arbia, G., & Espa, G. (2021). Advances in spatial economic data analysis: Methods and applications. Spatial Economic Analysis, 16(2), 121–125. https://doi.org/10.1080/17421772.2021.1883102
   Web of Science ®Google Scholar
• Piedra-Peña, J. (2023). Spatial dependence in patient migration flows. Spatial Economic Analysis, 18(4), 575–599. https://doi.org/10.1080/17421772.2023.2204122
   Web of Science ®Google Scholar
• Prucha, I. R. (2014). Instrumental variables/method of moments estimation. In M. M. Fischer, & P. Nijkamp (Eds.), Handbook of regional science (Vol. 3, pp. 1597–1617). Springer.
   Google Scholar
• Qu, X., & Lee, L.-F. (2015). Estimating a spatial autoregressive model with an endogenous spatial weight matrix. Journal of Econometrics, 184(2), 209–232. https://doi.org/10.1016/j.jeconom.2014.08.008
   Web of Science ®Google Scholar
• Qu, X., Wang, X., & Lee, L. (2016). Instrumental variables estimation of a spatial dynamic panel model with endogenous spatial weights when T is small. The Econometrics Journal, 19(3), 261–290. https://doi.org/10.1111/ectj.12069
   Web of Science ®Google Scholar
• Reed, W. R. (2015). On the practice of lagging variables to avoid simultaneity. Oxford Bulletin of Economics and Statistics, 77(6), 897–905. https://doi.org/10.1111/obes.12088
   Web of Science ®Google Scholar
• Rios, V., Pascual, P., & Cabases, F. (2017). What drives local government spending in Spain? A dynamic spatial panel approach. Spatial Economic Analysis, 12(2–3), 230–250. https://doi.org/10.1080/17421772.2017.1282166
   Web of Science ®Google Scholar
• Rodríguez, M., Carabali, J., Pérez, A., & Meneses, L. (2023). Migration and academic performance in higher education: Evidence for Colombia. Spatial Economic Analysis, 18(4), 1–19. https://doi.org/10.1080/17421772.2023.2214600
   Web of Science ®Google Scholar
• Rokicki, B., & Cieślik, A. (2022). Rethinking regional wage determinants: Regional market potential versus trade partners’ potential. Spatial Economic Analysis, 18(1), 126–142. https://doi.org/10.1080/17421772.2022.2070657
   Web of Science ®Google Scholar
• Schleypen, J. R., Mistry, M. N., Saeed, F., & Dasgupta, S. (2022). Sharing the burden: Quantifying climate change spillovers in the European Union under the Paris agreement. Spatial Economic Analysis, 17(1), 76–82. https://doi.org/10.1080/17421772.2021.1904150
   Web of Science ®Google Scholar
• Shi, W., & Lee, L. F. (2017). Spatial dynamic panel data model with interactive fixed effects. Journal of Econometrics, 197(2), 323–347. https://doi.org/10.1016/j.jeconom.2016.12.001
   Web of Science ®Google Scholar
• Souza, B., & Haddad, E. (2022). Climate change in Brazil: Dealing with uncertainty in agricultural productivity models and the implications for economy-wide impacts. Spatial Economic Analysis, 17(2), 83–100. https://doi.org/10.1080/17421772.2021.1934524
   Google Scholar
• Szumilo, N. (2021). Prices of peers: Identifying endogenous price effects in the housing market. Economic Journal, 131(639), 3041–3070. https://doi.org/10.1093/ej/ueaa129
   Web of Science ®Google Scholar
• Tan, C. (2023). Modelling spatial weight matrices and lags in spatial panel models [dissertation]. University of Groningen. https://doi.org/10.33612/diss.630685888
   Google Scholar
• Tan, C., & Elhorst, J. P. (2023). The spatial autoregressive panel data model with spatial moving average errors. Geographical Analysis. https://doi.org/10.1111/gean.12369
   PubMed Web of Science ®Google Scholar
• Taşpınar, S., Doğan, O., & Bera, A. K. (2019). Heteroskedasticity-consistent covariance matrix estimators for spatial autoregressive models. Spatial Economic Analysis, 14(2), 241–268. https://doi.org/10.1080/17421772.2019.1549366
   Web of Science ®Google Scholar
• Temursho, U., Oosterhaven, J., & Cardenete, M. A. (2020). A multi-regional generalized RAS updating technique. Spatial Economic Analysis, 16(3), 271–286. https://doi.org/10.1080/17421772.2020.1825782
   Web of Science ®Google Scholar
• Tobler, W. R. (1970). A computer movie simulating urban growth in the Detroit region. Economic Geography, 46, 234–240. https://doi.org/10.2307/143141
   Web of Science ®Google Scholar
• Tobler, W. R. (1999). Linear pycnophylactic reallocation comment on a paper by D. Martin. International Journal of Geographical Information Science, 13(1), 85–90. https://doi.org/10.1080/136588199241472
   Web of Science ®Google Scholar
• Torój, A. (2021). Construction of multiregion–multisector input–output tables: A spatial econometric approach for Poland. Spatial Economic Analysis, 16(4), 550–569. https://doi.org/10.1080/17421772.2021.1904149
   Web of Science ®Google Scholar
• Tziolas, I., & Elhorst, J. P. (2022). The rise and spread of female labour force participation at the U. S. county level. Spatial Economic Analysis, 18(1), 143–151. https://doi.org/10.1080/17421772.2022.2123111
   Web of Science ®Google Scholar
• Wang, H. (2017). Pricing used books on Amazon.com: A spatial approach to price dispersion. Spatial Economic Analysis, 13(1), 99–117. https://doi.org/10.1080/17421772.2017.1369147
   Web of Science ®Google Scholar
• Wang, W., & Lee, L.-F. (2017). GMM estimation of spatial panel data models with common factors and a general space–time filter. Spatial Economic Analysis, 13(2), 247–269. https://doi.org/10.1080/17421772.2017.1353128
   Web of Science ®Google Scholar
• Wang, Z., Graaff, T. d., & Nijkamp, P. (2015). Cultural diversity and cultural distance as choice determinants of migration destination. Spatial Economic Analysis, 11(2), 176–200. https://doi.org/10.1080/17421772.2016.1102956
   Web of Science ®Google Scholar
• Yilanci, V., Ulucak, R., & Ozgur, O. (2022). Insights for a sustainable environment: Analysing the persistence of policy shocks to ecological footprints of Mediterranean countries. Spatial Economic Analysis, 17(1), 47–66. https://doi.org/10.1080/17421772.2021.1919313
   Web of Science ®Google Scholar
• Zhu, B. (2018). The multi-country transmission of sovereign and banking risk: A spatial vector autoregressive approach. Spatial Economic Analysis, 13(4), 422–441. https://doi.org/10.1080/17421772.2018.1473890
   Web of Science ®Google Scholar