The regional pervasiveness of local productivity shocks on macroeconomic output in Europe

REFERENCES

• Acemoglu, D., Akcigit, U., & Kerr, W. (2016). Networks and the macroeconomy: An empirical exploration. NBER Macroeconomics Annual, 30(1), 273–335. https://doi.org/10.1086/685961
   Web of Science ®Google Scholar
• Acemoglu, D., & Azar, P. D. (2020). Endogenous production networks. Econometrica, 88(1), 33–82. https://doi.org/10.3982/ECTA15899
   Web of Science ®Google Scholar
• Acemoglu, D., Carvalho, V. M., Ozdaglar, A., & Tahbaz-Salehi, A. (2012). The network origins of aggregate fluctuations. Econometrica, 80(5), 1977–2016. https://doi.org/10.3982/ECTA9623
   Web of Science ®Google Scholar
• Anselin, L. (1988). Spatial econometrics: Methods and models. Springer Netherlands.
   Google Scholar
• Bahar, D., & Rapoport, H. (2018). Migration, knowledge diffusion and the comparative advantage of nations. The Economic Journal, 128(612), F273–F305. https://doi.org/10.1111/ecoj.12450
   Web of Science ®Google Scholar
• Bailey, N., Holly, S., & Pesaran, M. H. (2015a). A two-stage approach to spatio-temporal analysis with strong and weak cross-sectional dependence. Journal of Applied Econometrics, 31(1), 249–280. https://doi.org/10.1002/jae.2468
   Web of Science ®Google Scholar
• Bailey, N., Kapetanios, G., & Pesaran, M. H. (2015b). Exponent of cross-sectional dependence: Estimation and inference. Journal of Applied Econometrics, 31(6), 929–960. https://doi.org/10.1002/jae.2476
   Web of Science ®Google Scholar
• Bailey, N., Pesaran, M. H., & Smith, L. V. (2019). A multiple testing approach to the regularisation of large sample correlation matrices. Journal of Econometrics, 208(2), 507–534. https://doi.org/10.1016/j.jeconom.2018.10.006
   Web of Science ®Google Scholar
• Baltagi, B. H., Fingleton, B., & Pirotte, A. (2019). A time-space dynamic panel data model with spatial moving average errors. Regional Science and Urban Economics, 76, 13–31. https://doi.org/10.1016/j.regsciurbeco.2018.04.013
   Web of Science ®Google Scholar
• Banerjee, A., & Carrion-i-Silvestre, J. L. C. (2017). Testing for panel cointegration using common correlated effects estimators. Journal of Time Series Analysis, 38(4), 610–636. https://doi.org/10.1111/jtsa.12234
   Web of Science ®Google Scholar
• Bottazzi, L., & Peri, G. (2003). Innovation and spillovers in regions: Evidence from European patent data. European Economic Review, 47(4), 687–710. https://doi.org/10.1016/S0014-2921(02)00307-0
   Web of Science ®Google Scholar
• Brownlees, C., & Mesters, G. (2021). Detecting granular time series in large panels. Journal of Econometrics, 220(2), 544–561. https://doi.org/10.1016/j.jeconom.2020.04.013
   Web of Science ®Google Scholar
• Charlot, S., Crescenzi, R., & Musolesi, A. (2015). Econometric modelling of the regional knowledge production function in Europe. Journal of Economic Geography, 15(6), 1227–1259. https://doi.org/10.1093/jeg/lbu035
   Web of Science ®Google Scholar
• Chudik, A., & Pesaran, M. H. (2013). Econometric analysis of high dimensional VARs featuring a dominant unit. Econometric Reviews, 32(5–6), 592–649. https://doi.org/10.1080/07474938.2012.740374
   Web of Science ®Google Scholar
• Chudik, A., Pesaran, M. H., & Tosetti, E. (2011). Weak and strong cross-section dependence and estimation of large panels. The Econometrics Journal, 14(1), C45–C90. https://doi.org/10.1111/j.1368-423X.2010.00330.x
   Web of Science ®Google Scholar
• Coe, D. T., & Helpman, E. (1995). International R&D spillovers. European Economic Review, 39(5), 859–887. https://doi.org/10.1016/0014-2921(94)00100-E
   Web of Science ®Google Scholar
• Coe, D. T., Helpman, E., & Hoffmaister, A. W. (2009). International R&D spillovers and institutions. European Economic Review, 53(7), 723–741. https://doi.org/10.1016/j.euroecorev.2009.02.005
   Web of Science ®Google Scholar
• Conley, T. (1999). GMM estimation with cross sectional dependence. Journal of Econometrics, 92(1), 1–45. https://doi.org/10.1016/S0304-4076(98)00084-0
   Web of Science ®Google Scholar
• Corrado, L., & Fingleton, B. (2011). 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
• Correia, S., Guimarães, P., & Zylkin, T. (2020). Fast poisson estimation with high-dimensional fixed effects. The Stata Journal: Promoting communications on statistics and Stata, 20(1), 95–115. https://doi.org/10.1177/1536867X20909691
   Google Scholar
• Dettori, B., Marrocu, E., & Paci, R. (2012). Total factor productivity, intangible assets and spatial dependence in the European regions. Regional Studies, 46(10), 1401–1416. https://doi.org/10.1080/00343404.2010.529288
   Web of Science ®Google Scholar
• di Giovanni, J., & Levchenko, A. A. (2012). Country size, international trade, and aggregate fluctuations in granular economies. Journal of Political Economy, 120(6), 1083–1132. https://doi.org/10.1086/669161
   Web of Science ®Google Scholar
• Eaton, J., & Kortum, S. (2002). Technology, geography, and trade. Econometrica, 70(5), 1741–1779. https://doi.org/10.1111/1468-0262.00352
   Web of Science ®Google Scholar
• Eeckhout, J. (2004). Gibrat’s law for (all) cities. American Economic Review, 94(5), 1429–1451. https://doi.org/10.1257/0002828043052303
   Web of Science ®Google Scholar
• Ertur, C., & Koch, W. (2011). A contribution to the theory and empirics of Schumpeterian growth with worldwide interactions. Journal of Economic Growth, 16(3), 215–255. https://doi.org/10.1007/s10887-011-9067-0
   Web of Science ®Google Scholar
• Ertur, C., & Musolesi, A. (2016). Weak and strong cross-sectional dependence: A panel data analysis of international technology diffusion. Journal of Applied Econometrics, 32(3), 477–503. https://doi.org/10.1002/jae.2538
   Web of Science ®Google Scholar
• Fally, T. (2015). Structural gravity and fixed effects. Journal of International Economics, 97(1), 76–85. https://doi.org/10.1016/j.jinteco.2015.05.005
   Web of Science ®Google Scholar
• Fingleton, B., Garretsen, H., & Martin, R. (2015). Shocking aspects of monetary union: The vulnerability of regions in Euroland. Journal of Economic Geography, 15(5), 907–934. https://doi.org/10.1093/jeg/lbu055
   Web of Science ®Google Scholar
• Frankel, J. A., & Romer, D. (2017). Does trade cause growth? In J. J. Kirton (Ed.), Global trade (pp. 255–276). Routledge.
   Google Scholar
• Gabaix, X. (2011). The granular origins of aggregate fluctuations. Econometrica, 79(3), 733–772. https://doi.org/10.3982/ECTA8769
   Web of Science ®Google Scholar
• Gabaix, X., & Ibragimov, R. (2011). Rank - 1/2: A simple way to improve the OLS estimation of tail exponents. Journal of Business & Economic Statistics, 29(1), 24–39. https://doi.org/10.1198/jbes.2009.06157
   Web of Science ®Google Scholar
• Gaubert, C., & Itskhoki, O. (2021). Granular comparative advantage. Journal of Political Economy, 129(3), 871–939. https://doi.org/10.1086/712444
   Web of Science ®Google Scholar
• Gioldasis, G., Musolesi, A., & Simioni, M. (2023). Interactive r&d spillovers: An estimation strategy based on forecasting-driven model selection. International Journal of Forecasting. https://doi.org/10.1016/j.ijforecast.2021.09.009
   Web of Science ®Google Scholar
• Halleck-Vega, S., & Elhorst, J. P. (2015). The slx model. Journal of Regional Science, 55(3), 339–363. https://doi.org/10.1111/jors.12188
   Web of Science ®Google Scholar
• Head, K., & Mayer, T. (2014). Gravity equations: Workhorse, toolkit, and cookbook. In V. Henderson & J.-F. Thisse (Eds.), Handbook of international economics (pp. 131–195). Elsevier.
   Google Scholar
• Head, K., & Mayer, T. (2021). The United States of Europe: A gravity model evaluation of the four freedoms. Journal of Economic Perspectives, 35(2), 23–48. https://doi.org/10.1257/jep.35.2.23
   Web of Science ®Google Scholar
• Hill, B. M. (1975). A simple general approach to inference about the tail of a distribution. The Annals of Statistics, 3(5), 1163–1174.
   Web of Science ®Google Scholar
• Ho, C.-Y., Wang, W., & Yu, J. (2018). International knowledge spillover through trade: A time-varying spatial panel data approach. Economics Letters, 162, 30–33. https://doi.org/10.1016/j.econlet.2017.10.015
   Web of Science ®Google Scholar
• Holly, S., Pesaran, M. H., & Yamagata, T. (2011). The spatial and temporal diffusion of house prices in the UK. Journal of Urban Economics, 69(1), 2–23. https://doi.org/10.1016/j.jue.2010.08.002
   Web of Science ®Google Scholar
• Juodis, A., & Reese, S. (2022). The incidental parameters problem in testing for remaining cross-section correlation. Journal of Business & Economic Statistics, 40(3), 1191–1203. https://doi.org/10.1080/07350015.2021.1906687
   Web of Science ®Google Scholar
• Kapetanios, G., Pesaran, M. H., & Reese, S. (2021). Detection of units with pervasive effects in large panel data models. Journal of Econometrics, 221(2), 510–541. https://doi.org/10.1016/j.jeconom.2020.05.001
   Web of Science ®Google Scholar
• Kelejian, H. H., & Prucha, I. R. (2001). On the asymptotic distribution of the Moran I test statistic with applications. Journal of Econometrics, 104(2), 219–257. https://doi.org/10.1016/S0304-4076(01)00064-1
   Web of Science ®Google Scholar
• Keller, W. (2002). Geographic localization of international technology diffusion. American Economic Review, 92(1), 120–142. https://doi.org/10.1257/000282802760015630
   Web of Science ®Google Scholar
• Keller, W. (2004). International technology diffusion. Journal of Economic Literature, 42(3), 752–782. https://doi.org/10.1257/0022051042177685
   Web of Science ®Google Scholar
• Keller, W. (2010). International trade, foreign direct investment, and technology spillovers. In B. H. Hall & N. Rosenberg (Eds.), Handbook of the economics of innovation (Vol. 2, pp. 793–829). Elsevier.
   Google Scholar
• Lee, L.-f., Yang, C., & Yu, J. (2023). QML and efficient GMM estimation of spatial autoregressive models with dominant (popular) units. Journal of Business & Economic Statistics, 1–13. https://doi.org/10.1080/07350015.2022.2041424
   Web of Science ®Google Scholar
• LeSage, J., & Pace, R. K. (2009). Introduction to spatial econometrics (statistics: A series of textbooks and monographs). Chapman and Hall/CRC.
   Google Scholar
• Levy, M. (2009). Gibrat’s law for (all) cities: Comment. American Economic Review, 99(4), 1672–1675. https://doi.org/10.1257/aer.99.4.1672
   Web of Science ®Google Scholar
• Long, J. B., & Plosser, C. I. (1983). Real business cycles. Journal of Political Economy, 91(1), 39–69. https://doi.org/10.1086/261128
   Web of Science ®Google Scholar
• Lucas, R. E. (1977). Understanding business cycles. Carnegie-Rochester Conference Series on Public Policy, 5, 7–29. https://doi.org/10.1016/0167-2231(77)90002-1
   Google Scholar
• Marrocu, E., Paci, R., & Usai, S. (2013). Productivity growth in the old and new Europe: The role of agglomeration externalities. Journal of Regional Science, 53(3), 418–442. https://doi.org/10.1111/jors.12000
   Web of Science ®Google Scholar
• Männasoo, K., Hein, H., & Ruubel, R. (2018). The contributions of human capital, r&d spending and convergence to total factor productivity growth. Regional Studies, 52(12), 1598–1611. https://doi.org/10.1080/00343404.2018.1445848
   Web of Science ®Google Scholar
• McCallum, J. (1995). National borders matter: Canada-us regional trade patterns. The American Economic Review, 85(3), 615–623.
   Web of Science ®Google Scholar
• Paci, R., Marrocu, E., & Usai, S. (2014). The complementary effects of proximity dimensions on knowledge spillovers. Spatial Economic Analysis, 9(1), 9–30. https://doi.org/10.1080/17421772.2013.856518
   Web of Science ®Google Scholar
• Pedroni, P. (1999). Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxford Bulletin of Economics and Statistics, 61(s1), 653–670. https://doi.org/10.1111/1468-0084.61.s1.14
   Google Scholar
• Pedroni, P. (2004). Panel cointegration: Asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis. Econometric Theory, 20(03|3), 03. https://doi.org/10.1017/S0266466604203073
   Web of Science ®Google Scholar
• Pesaran, M. H. (2006). Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica, 74(4), 967–1012. https://doi.org/10.1111/j.1468-0262.2006.00692.x
   Web of Science ®Google Scholar
• Pesaran, M. H. (2014). Testing weak cross-sectional dependence in large panels. Econometric Reviews, 34(6–10), 1089–1117. https://doi.org/10.1080/07474938.2014.956623
   Web of Science ®Google Scholar
• Pesaran, M. H. (2015a). Testing weak cross-sectional dependence in large panels. Econometric Reviews, 34(6–10), 1089–1117. https://doi.org/10.1080/07474938.2014.956623
   Web of Science ®Google Scholar
• Pesaran, M. H. (2015b). Time series and panel data econometrics. Oxford University Press.
   Google Scholar
• Pesaran, M. H., & Tosetti, E. (2011). Large panels with common factors and spatial correlation. Journal of Econometrics, 161(2), 182–202. https://doi.org/10.1016/j.jeconom.2010.12.003
   Web of Science ®Google Scholar
• Pesaran, M. H., & Yang, C. F. (2020). Econometric analysis of production networks with dominant units. Journal of Econometrics, 219(2), 507–541. https://doi.org/10.1016/j.jeconom.2020.03.014
   Web of Science ®Google Scholar
• Pesaran, M. H., & Yang, C. F. (2021). Estimation and inference in spatial models with dominant units. Journal of Econometrics, 221(2), 591–615. https://doi.org/10.1016/j.jeconom.2020.04.045
   Web of Science ®Google Scholar
• Pesaran, M., & Smith, R. (1995). Estimating long-run relationships from dynamic heterogeneous panels. Journal of Econometrics, 68(1), 79–113. https://doi.org/10.1016/0304-4076(94)01644-F
   Web of Science ®Google Scholar
• Qu, X., fei Lee, L., & Yang, C. (2021). Estimation of a SAR model with endogenous spatial weights constructed by bilateral variables. Journal of Econometrics, 221(1), 180–197. https://doi.org/10.1016/j.jeconom.2020.05.011
   Web of Science ®Google Scholar
• Santamaría, M. A., Ventura, J., & Yeşilbayraktar, U. (2021). Borders within Europe. Technical report, National Bureau of Economic Research.
   Google Scholar
• Siller, M., Schatzer, T., Walde, J., & Tappeiner, G. (2021). What drives total factor productivity growth? An examination of spillover effects. Regional Studies, 55(6), 1129–1139. https://doi.org/10.1080/00343404.2020.1869199
   Web of Science ®Google Scholar
• Silva, J. M. C. S., & Tenreyro, S. (2006). The log of gravity. Review of Economics and Statistics, 88(4), 641–658. https://doi.org/10.1162/rest.88.4.641
   Web of Science ®Google Scholar
• Thissen, M., Van Oort, F., Diodato, D., & Ruijs, A. (2013). Regional competitiveness and smart specialization in Europe: Place-based development in international economic networks. Edward Elgar Publishing.
   Google Scholar
• Zhao, S., Jin, M., & Kumbhakar, S. C. (2021). Estimation of firm productivity in the presence of spillovers and common shocks. Empirical Economics, 60(6), 3135–3170. https://doi.org/10.1007/s00181-020-01922-3
   Web of Science ®Google Scholar