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Estimating and Forecasting with a Dynamic Spatial Panel Data Model

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  1. Anna Gloria Billé & Alessio Tomelleri & Francesco Ravazzolo, 2023. "Forecasting regional GDPs: a comparison with spatial dynamic panel data models," Spatial Economic Analysis, Taylor & Francis Journals, vol. 18(4), pages 530-551, October.
  2. Baltagi, Badi H. & Fingleton, Bernard & Pirotte, Alain, 2019. "A time-space dynamic panel data model with spatial moving average errors," Regional Science and Urban Economics, Elsevier, vol. 76(C), pages 13-31.
  3. Jean-Sauveur Ay & Raja Chakir & Julie Le Gallo, 2014. "The effects of scale, space and time on the predictive accuracy of land use models," Working Papers 2014/02, INRA, Economie Publique.
  4. W. Saart, Patrick & Kim, Namhyun & Bateman, Ian, 2021. "Understanding spatial heterogeneity in GB agricultural land-use for improved policy targeting," Cardiff Economics Working Papers E2021/8, Cardiff University, Cardiff Business School, Economics Section.
  5. Brandeis, Consuelo & Lambert, Dayton M., 2014. "Projecting county pulpwood production with historical production and macro-economic variables," Journal of Forest Economics, Elsevier, vol. 20(3), pages 305-315.
  6. Jean-François Richard, 2015. "Likelihood Based Inference and Prediction in Spatio-temporal Panel Count Models for Urban Crimes," Working Paper 5657, Department of Economics, University of Pittsburgh.
  7. Lee, Lung-fei & Yu, Jihai, 2015. "Estimation of fixed effects panel regression models with separable and nonseparable space–time filters," Journal of Econometrics, Elsevier, vol. 184(1), pages 174-192.
  8. Torben Dall Schmidt & Aki Kangasharju & Timo Mitze & Daniel Rauhut, 2014. "The impact of aging on regional employment: Linking spatial econometrics and population projections for a scenario analysis of future labor market outcomes in Nordic regions," Economics and Business Letters, Oviedo University Press, vol. 3(4), pages 232-246.
  9. Bernard Fingleton, 2014. "Forecasting with dynamic spatial panel data: practical implementation methods," Economics and Business Letters, Oviedo University Press, vol. 3(4), pages 194-207.
  10. Reinhold Kosfeld & Christian Dreger, 2018. "Local and spatial cointegration in the wage curve – a spatial panel analysis for german regions," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 38(1), pages 53-75, February.
  11. Justin Doran & Bernard Fingleton, 2018. "US Metropolitan Area Resilience: Insights from dynamic spatial panel estimation," Environment and Planning A, , vol. 50(1), pages 111-132, February.
  12. Yu, Dalei & Bai, Peng & Ding, Chang, 2015. "Adjusted quasi-maximum likelihood estimator for mixed regressive, spatial autoregressive model and its small sample bias," Computational Statistics & Data Analysis, Elsevier, vol. 87(C), pages 116-135.
  13. Paelinck, Jean & Mur, Jesús & Trivez, F. Javier, 2015. "Modelos para datos espaciales con estructura transversal o de panel. Una revisión/Models for Spatial Data with Panel or Cross-Sectional Structure. A Review," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 33, pages 7-30, Enero.
  14. Wozniak Marcin, 2020. "Forecasting the unemployment rate over districts with the use of distinct methods," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(2), pages 1-20, April.
  15. Baltagi, Badi H., 2013. "Panel Data Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 995-1024, Elsevier.
  16. Badi H. Baltagi & Bernard Fingleton & Alain Pirotte, 2014. "Estimating and Forecasting with a Dynamic Spatial Panel Data Model," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(1), pages 112-138, February.
  17. Chian-Yue Wang & Robert Haining, 2017. "Testing the new economic geography’s wage equation: a case study of Japan using a spatial panel model," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 58(3), pages 417-440, May.
  18. M. Mokliak, P. Chernov, A. Vdovychenko, A. Zubritskyi, 2015. "Spatial approach in forecasting tax revenues," Economy and Forecasting, Valeriy Heyets, issue 2, pages 7-20.
  19. Gopal K. Basak & Arnab Bhattacharjee & Samarjit Das, 2018. "Causal ordering and inference on acyclic networks," Empirical Economics, Springer, vol. 55(1), pages 213-232, August.
  20. Bronić Mihaela & Stanić Branko & Prijaković Simona, 2022. "The Effects of Budget Transparency on the Budget Balances and Expenditures of Croatian Local Governments," South East European Journal of Economics and Business, Sciendo, vol. 17(1), pages 111-124, June.
  21. Bernard Fingleton & Franz Fuerst & Nikodem Szumilo, 2019. "Housing affordability: Is new local supply the key?," Environment and Planning A, , vol. 51(1), pages 25-50, February.
  22. Atems, Bebonchu, 2013. "The spatial dynamics of growth and inequality: Evidence using U.S. county-level data," Economics Letters, Elsevier, vol. 118(1), pages 19-22.
  23. Ana Angulo & Jesús Mur & Javier Trivez, 2014. "Measure of the resilience to Spanish economic crisis: the role of specialization," Economics and Business Letters, Oviedo University Press, vol. 3(4), pages 263-275.
  24. Fingleton Bernard & Gardiner Ben & Martin Ron & Barbieri Luca, 2023. "The impact of brexit on regional productivity in the UK," ZFW – Advances in Economic Geography, De Gruyter, vol. 67(2-3), pages 142-160, August.
  25. Kripfganz, Sebastian, 2014. "Unconditional Transformed Likelihood Estimation of Time-Space Dynamic Panel Data Models," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100604, Verein für Socialpolitik / German Economic Association.
  26. You, Jing & Huang, Yongfu, 2013. "Green-to-Grey China: Determinants and Forecasts of its Green Growth," MPRA Paper 57468, University Library of Munich, Germany, revised 16 Jul 2014.
  27. Subhash C. Sharma & Anil K. Bera, 2021. "Estimation of Random Components and Prediction in One and Two-Way Error Component Regression Models," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(1), pages 419-441, December.
  28. Vomfell, Lara & Härdle, Wolfgang Karl & Lessmann, Stefan, 2018. "Improving Crime Count Forecasts Using Twitter and Taxi Data," IRTG 1792 Discussion Papers 2018-013, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
  29. Mitze, Timo & Makkonen, Teemu, 2023. "Can large-scale RDI funding stimulate post-crisis recovery growth? Evidence for Finland during COVID-19," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
  30. Alejandro Barragán-Ocaña & Gerardo Reyes-Ruiz & Samuel Olmos-Peña & Hortensia Gómez-Viquez, 2020. "Approach to the identification of an alternative technological innovation index," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 23-45, January.
  31. Marcos-Martinez, Raymundo & Bryan, Brett A. & Schwabe, Kurt A. & Connor, Jeffery D. & Law, Elizabeth A., 2018. "Forest transition in developed agricultural regions needs efficient regulatory policy," Forest Policy and Economics, Elsevier, vol. 86(C), pages 67-75.
  32. Álvarez, Inmaculada C. & Barbero, Javier & Zofío, José L., 2016. "A spatial autoregressive panel model to analyze road network spillovers on production," Transportation Research Part A: Policy and Practice, Elsevier, vol. 93(C), pages 83-92.
  33. Xianning WANG & Jingrong DONG & Zhi XIAO & Guanjie HE, 2019. "A novel spatial mixed frequency forecasting model with application to Chinese regional GDP," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 54-77, June.
  34. Lehel Györfy & Szilárd Madaras, 2017. "Factors Influencing Nuts3 Level Regional Competitiveness In Center Region, Romania. A Panel Regression Analysis," Romanian Journal of Regional Science, Romanian Regional Science Association, vol. 11(2), pages 47-61, December.
  35. Süleyman Taşpınar & Osman DoĞan & Jiyoung Chae & Anil K. Bera, 2021. "Bayesian Inference in Spatial Stochastic Volatility Models: An Application to House Price Returns in Chicago," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(5), pages 1243-1272, October.
  36. Federico Lampis, 2016. "Forecasting the sectoral GVA of a small Spanish region," Economics and Business Letters, Oviedo University Press, vol. 5(2), pages 38-44.
  37. Roberto Patuelli & Matías Mayor, 2014. "Introduction," Economics and Business Letters, Oviedo University Press, vol. 3(4), pages 191-193.
  38. Giovanni S F Bruno & Enrico Marelli & Marcello Signorelli, 2014. "The Rise of NEET and Youth Unemployment in EU Regions after the Crisis," Comparative Economic Studies, Palgrave Macmillan;Association for Comparative Economic Studies, vol. 56(4), pages 592-615, December.
  39. Müller, Jonas & Trutnevyte, Evelina, 2020. "Spatial projections of solar PV installations at subnational level: Accuracy testing of regression models," Applied Energy, Elsevier, vol. 265(C).
  40. Hao, Yu & Zhang, Zong-Yong & Liao, Hua & Wei, Yi-Ming, 2015. "China’s farewell to coal: A forecast of coal consumption through 2020," Energy Policy, Elsevier, vol. 86(C), pages 444-455.
  41. Valerij Gamukin, 2017. "Structural Change of Gross Regional Product in the Subjects of Ural Federal District," Economy of region, Centre for Economic Security, Institute of Economics of Ural Branch of Russian Academy of Sciences, vol. 1(2), pages 410-421.
  42. Leopoldo Catania & Anna Gloria Billé, 2017. "Dynamic spatial autoregressive models with autoregressive and heteroskedastic disturbances," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(6), pages 1178-1196, September.
  43. Bernard Fingleton, 2020. "Exploring Brexit with dynamic spatial panel models: some possible outcomes for employment across the EU regions," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 64(2), pages 455-491, April.
  44. Wongsa-art, Pipat & Kim, Namhyun & Xia, Yingcun & Moscone, Francesco, 2024. "Varying coefficient panel data models and methods under correlated error components: Application to disparities in mental health services in England," Regional Science and Urban Economics, Elsevier, vol. 106(C).
  45. Vincent Boucher & Marion Gousse, 2019. "Wage Dynamics and Peer Referrals," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 31, pages 1-23, January.
  46. Roman Liesenfeld & Jean‐François Richard & Jan Vogler, 2017. "Likelihood‐Based Inference and Prediction in Spatio‐Temporal Panel Count Models for Urban Crimes," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(3), pages 600-620, April.
  47. Fingleton, Bernard & Szumilo, Nikodem, 2019. "Simulating the impact of transport infrastructure investment on wages: A dynamic spatial panel model approach," Regional Science and Urban Economics, Elsevier, vol. 75(C), pages 148-164.
  48. Fonseca Morello, Thiago & Marchetti Ramos, Rossano & O. Anderson, Liana & Owen, Nathan & Rosan, Thais Michele & Steil, Lara, 2020. "Predicting fires for policy making: Improving accuracy of fire brigade allocation in the Brazilian Amazon," Ecological Economics, Elsevier, vol. 169(C).
  49. Akgun, Oguzhan & Pirotte, Alain & Urga, Giovanni, 2020. "Forecasting using heterogeneous panels with cross-sectional dependence," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1211-1227.
  50. Shrestha, Anil & Mustafa, Andy Ali & Htike, Myo Myo & You, Vithyea & Kakinaka, Makoto, 2022. "Evolution of energy mix in emerging countries: Modern renewable energy, traditional renewable energy, and non-renewable energy," Renewable Energy, Elsevier, vol. 199(C), pages 419-432.
  51. Rodolfo Metulini, 2013. "Spatial gravity models for international trade: a panel analysis among OECD countries," ERSA conference papers ersa13p522, European Regional Science Association.
  52. J. Wesley Burnett & Xueting Zhao, 2014. "Forecasting U.S. State-Level Carbon Dioxide Emissions," The Review of Regional Studies, Southern Regional Science Association, vol. 44(3), pages 223-240, Winter.
  53. Robert Lehmann & Klaus Wohlrabe, 2014. "Regional economic forecasting: state-of-the-art methodology and future challenges," Economics and Business Letters, Oviedo University Press, vol. 3(4), pages 218-231.
  54. W. Saart, Patrick & Kim, Namhyun & Bateman, Ian, 2021. "Modeling and predicting agricultural land use in England based on spatially high-resolution data," Cardiff Economics Working Papers E2021/7, Cardiff University, Cardiff Business School, Economics Section.
  55. Wolter, James Lewis, 2016. "Kernel estimation of hazard functions when observations have dependent and common covariates," Journal of Econometrics, Elsevier, vol. 193(1), pages 1-16.
  56. Ana Barufi & Eduardo Haddad & Peter Nijkamp, 2016. "A comprehensive analysis of the wage curve in Brazil: Non-linearities, urban size, and the spatial dimension," ERSA conference papers ersa16p279, European Regional Science Association.
  57. Leal, Ana R. & Husted, Bryan W. & Flores Segovia, Miguel Alejandro, 2021. "Environmental performance spillovers among Mexican industrial facilities: The case of greenhouse gases," Journal of Business Research, Elsevier, vol. 135(C), pages 711-720.
  58. Semerikova, Elena & Demidova, Olga, 2016. "Using spatial econometric models for regional unemployment forecasting," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 43, pages 29-51.
  59. Christian Glocker & Matteo Iacopini & Tam'as Krisztin & Philipp Piribauer, 2023. "A Bayesian Markov-switching SAR model for time-varying cross-price spillovers," Papers 2310.19557, arXiv.org.
  60. Gong, Pu & Weng, Yingliang, 2016. "Value-at-Risk forecasts by a spatiotemporal model in Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 441(C), pages 173-191.
  61. Bernard Fingleton, 2022. "Modifying the linear two-step Windmeijer correction for the presence of spatial error dependence," Journal of Spatial Econometrics, Springer, vol. 3(1), pages 1-18, December.
  62. Ye Yang & Osman Doğan & Süleyman Taşpınar, 2023. "Observed-data DIC for spatial panel data models," Empirical Economics, Springer, vol. 64(3), pages 1281-1314, March.
  63. Baltagi, Badi H. & Pirotte, Alain, 2014. "Prediction in a spatial nested error components panel data model," International Journal of Forecasting, Elsevier, vol. 30(3), pages 407-414.
  64. Lung‐fei Lee & Jihai Yu, 2012. "Spatial Panels: Random Components Versus Fixed Effects," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 53(4), pages 1369-1412, November.
  65. Sommer, Benedikt & Pinson, Pierre & Messner, Jakob W. & Obst, David, 2021. "Online distributed learning in wind power forecasting," International Journal of Forecasting, Elsevier, vol. 37(1), pages 205-223.
  66. Xueting Zhao & J. Burnett, 2014. "Forecasting province-level $${\text {CO}}_{2}$$ CO 2 emissions in China," Letters in Spatial and Resource Sciences, Springer, vol. 7(3), pages 171-183, October.
  67. Burridge, Peter & Iacone, Fabrizio & Lazarová, Štěpána, 2015. "Spatial effects in a common trend model of US city-level CPI," Regional Science and Urban Economics, Elsevier, vol. 54(C), pages 87-98.
  68. James Wolter, 2015. "Kernel Estimation Of Hazard Functions When Observations Have Dependent and Common Covariates," Economics Series Working Papers 761, University of Oxford, Department of Economics.
  69. Bernard Fingleton, 2020. "Italexit, is it another Brexit?," Journal of Geographical Systems, Springer, vol. 22(1), pages 77-104, January.
  70. Bernard Fingleton, 2016. "Regional Science in a time of uncertainty," REGION, European Regional Science Association, vol. 3, pages 61-69.
  71. Atems, Bebonchu, 2015. "Another look at tax policy and state economic growth: The long-run and short-run of it," Economics Letters, Elsevier, vol. 127(C), pages 64-67.
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