REFERENCES
- Angulo, A., Burridge, P., & Mur, J. (2017). Testing for a structural break in the weight matrix of the spatial error or spatial lag model. Spatial Economic Analysis, 12(2–3), 161–181. doi:10.1080/17421772.2016.1264620
- Arbia, G. (2017). Effects of missing data and locational errors on spatial concentration measures based on Ripley’s K-function. Spatial Economic Analysis, 12(2–3), 326–346. doi:10.1080/17421772.2017.1297479
- Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58(2), 277–298. doi:10.2307/2297968
- Ay, J.-S., Chakir, R., Doyen, L., Jiguet, F., & Leadley, P. (2014). Integrated models, scenarios and dynamics of climate, land use and common birds. Climatic Change, 126(1–2), 13–30. doi:10.1007/s10584-014-1202-4
- Baltagi, B. H., Egger, P., & Pfaffermayr, M. (2013). A generalized spatial panel data model with random effects. Econometric Reviews, 32(5–6), 650–685. doi:10.1080/07474938.2012.742342
- Baltagi, B. H., Fingleton, B., & Pirotte, A. (2014). Estimating and forecasting with a dynamic spatial panel data model. Oxford Bulletin of Economics and Statistics, 76(1), 112–138. doi:10.1111/obes.12011
- Baltagi, B. H., Song, S. H., Jung, B. C., & Koh, W. (2007). Testing for serial correlation, spatial autocorrelation and random effects using panel data. Journal of Econometrics, 140(1), 5–51. doi:10.1016/j.jeconom.2006.09.001
- Baltagi, B. H., Song, S. H., & Koh, W. (2003). Testing panel data regression models with spatial error correlation. Journal of Econometrics, 117(1), 123–150. doi:10.1016/S0304-4076(03)00120-9
- Baltagi, B. H., Song, S. H., & Koh, W. H. (2009). Testing for heteroskedasticity and spatial correlation in a random effects panel data model. Computational Statistics and Data Analysis, 53(8), 2897–2922. doi:10.1016/j.csda.2008.06.009
- Baltagi, B. H., & Yang, Z. L. (2013). Standardized LM tests of spatial error dependence in linear or panel regressions. The Econometrics Journal, 16(1), 103–134. doi:10.1111/j.1368-423X.2012.00385.x
- Bhattacharjee, A., Cai, L., & Maiti, T. (2017). Functional regression over irregular domains: Variation in the shadow price of living space. Spatial Economic Analysis, 12(2–3), 182–201. doi:10.1080/17421772.2017.1286374
- Chakir, R., & Le Gallo, J. (2013). Predicting land use allocation in France: A spatial panel data analysis. Ecological Economics, 92(0), 114–125. doi:10.1016/j.ecolecon.2012.04.009
- Chakir, R., & Lungarska, A. (2017). Agricultural rent in land use models: Comparison of frequently used proxies. Spatial Economic Analysis, 12(2–3), 279–303. doi:10.1080/17421772.2017.1273542
- Davis, O. A., Dempster, M. A. H., & Wildavsky, A. (1966). A theory of the budgetary process. The American Political Science Review, 60(3), 529–547. doi:10.2307/1952969
- Dempster, M. A. H., & Wildavsky, A. (1979). On change: Or, there is no magic size for an increment. Political Studies, 27(3), 371–389. doi:10.1111/j.1467-9248.1979.tb01210.x
- Dezhbakhsh, H., Tohamy, S. M., & Aranson, P. H. (2003). A new approach for testing budgetary incrementalism. The Journal of Politics, 65(2), 532–558. doi:10.1111/1468-2508.t01-3-00014
- Egger, P., & Pfaffermayr, M. (2005). Estimating long and short run effects in static panel models. Econometric Reviews, 23(3), 199–214. doi:10.1081/ETC-200028201
- Elhorst, J. P. (2014). Spatial econometrics: From cross-sectional data to spatial panels. Dordrecht: Springer.
- Fiaschi, D., Gianmoena, L., & Parenti, A. (2017). Asymmetric macroeconomic volatility in European regions. Spatial Economic Analysis, 12(2–3), 251–278. doi:10.1080/17421772.2017.1276300
- Fingleton, B. (2001). Theoretical economic geography and spatial econometrics: Dynamic perspectives. Journal of Economic Geography, 1(2), 201–225. doi:10.1093/jeg/1.2.201
- Fingleton, B. (2004). Theoretical economic geography and spatial econometrics: Bridging the gap between theory and reality. In A. Getis, J. Mur, & H. Zoller (Eds.), Spatial econometrics and spatial statistics (pp. 8–27). New York: Palgrave.
- Firmino Corsta da Silva, D., Elhorst, J. P., & Neto Silveira, R.d.M. (2016). Urban and rural population growth in a spatial panel of municipalities. Regional Studies. Advance online publication. doi:10.1080/00343404.2016.1144922
- Goldberger, A. S. (1962). Best linear unbiased prediction in the generalized linear regression model. Journal of the American Statistical Association, 57(298), 369–375. doi:10.1080/01621459.1962.10480665
- Goulard, M., Laurent, T., & Thomas-Aignan, C. (2017). About predictions in spatial autoregressive models: Optimal and almost optimal strategies. Spatial Economic Analysis, 12(2–3), 304–325. doi:10.1080/17421772.2017.1300679
- Halleck Vega, S., & Elhorst, J. P. (2015). The SLX model. Journal of Regional Science, 55(3), 339–363. doi:10.1111/jors.12188
- Halleck Vega, S., & Elhorst, J. P. (2016). Regional labour force participation across the European Union: A time–space recursive modelling approach with endogenous regressors. Spatial Economic Analysis, 12(2–3), 138–160. doi:10.1080/17421772.2016.1224374
- Krige, D. G. (1966). Two-dimensional weighted moving average trend surfaces for ore valuation. Proceedings of the symposium on mathematical statistics and computer applications in Ore valuation, Johannesburg, pp. 13–38.
- Lee, L.-F., & Yu, J. (2010). Estimation of spatial autoregressive panel data models with fixed effects. Journal of Econometrics, 154(2), 165–185. doi:10.1016/j.jeconom.2009.08.001
- LeSage, J. P. (2014). Spatial econometric panel data model specification: A Bayesian approach. Spatial Statistics, 9(2), 122–145. doi:10.1016/j.spasta.2014.02.002
- LeSage, J. P., & Pace, R. K. (2009). Introduction to spatial econometrics. Boca Raton: Chapman & Hall/CRC Press.
- Lubowski, R. N., Plantinga, A. J., & Stavins, R. N. (2008). What drives land-use change in the United States? A national analysis of landowner decisions. Land Economics, 84(4), 529–550. doi:10.3368/le.84.4.529
- Pesaran, M. H. (2015). Time series and panel data econometrics. Oxford: Oxford University Press.
- Pirotte, A. (1999). Convergence of the static estimation toward the long run effects of dynamic panel data models. Economics Letters, 63(2), 151–158. doi:10.1016/S0165-1765(99)00023-3
- Pirotte, A., & Mur, J. (2016). Neglected dynamics and spatial dependence on panel data: Consequences for convergence of the usual static model estimators. Spatial Economic Analysis, 12(2–3), 202–229. doi:10.1080/17421772.2016.1232839
- Ramsay, T. (2002). Spline smoothing over difficult regions. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64(2), 307–319. doi:10.1111/1467-9868.00339
- Rickman, D. S. (2010). Modern macroeconomics and regional economic modeling. Journal of Regional Science, 50(1), 23–41. doi:10.1111/j.1467-9787.2009.00647.x
- 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. doi:10.1080/17421772.2017.1282166
- Ripley, B. D. (1976). The second-order analysis of stationary point processes. Journal of Applied Probability, 13(2), 255–266. doi:10.1017/S0021900200094328
- Ripley, B. D. (1977). Modelling spatial patterns (with discussion). Journal of the Royal Statistical Society: Series B, 39(2), 172–212. Retrieved from www.jstor.org/stable/2984796
- Sangalli, L. M., Ramsay, J. O., & Ramsay, T. O. (2013). Spatial spline regression models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 75(4), 681–703. doi:10.1111/rssb.12009
- Simon, H. A. (1995). Rationality in political behavior. Political Psychology, 16(1), 45–61. doi:10.2307/3791449
- Yang, Z. L. (2010). A robust LM test for spatial error components. Regional Science and Urban Economics, 40(5), 299–310. doi:10.1016/j.regsciurbeco.2009.10.001