IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Log in (now much improved!)

Citations for "Aggregation of space-time processes"

by Giacomini, Raffaella & Granger, Clive W. J.

For a complete description of this item, click here. For a RSS feed for citations of this item, click here.
as
in new window

  1. Shoesmith, Gary L., 2013. "Space–time autoregressive models and forecasting national, regional and state crime rates," International Journal of Forecasting, Elsevier, vol. 29(1), pages 191-201.
  2. Giacomini, Raffaella, 2014. "Economic theory and forecasting: lessons from the literature," CEPR Discussion Papers 10201, C.E.P.R. Discussion Papers.
  3. Kristie M. Engemann & Rubén Hernández-Murillo & Michael T. Owyang, 2008. "Regional aggregation in forecasting: an application to the Federal Reserve's Eighth District," Regional Economic Development, Federal Reserve Bank of St. Louis, issue Oct, pages 15-29.
  4. Auffhammer, Maximilian & Steinhauser, Ralf, 2006. "The Future Trajectory of US CO2 Emissions: The Role of State vs. Aggregate Information," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt4878j5w0, Department of Agricultural & Resource Economics, UC Berkeley.
  5. Hendry, David F. & Hubrich, Kirstin, 2010. "Combining disaggregate forecasts or combining disaggregate information to forecast an aggregate," Working Paper Series 1155, European Central Bank.
  6. Frédérick Demers & David Dupuis, 2005. "Forecasting Canadian GDP: Region-Specific versus Countrywide Information," Staff Working Papers 05-31, Bank of Canada.
  7. Bhattacharjee, A. & Holly, S., 2010. "Structural Interactions in Spatial Panels," Cambridge Working Papers in Economics 1004, Faculty of Economics, University of Cambridge.
  8. Duo Qin & Marie Anne Cagas & Geoffrey Ducanes & Nedelyn Magtibay-Ramos & Pilipinas F. Quising, 2006. "Measuring Regional Market Integration by Dynamic Factor Error Correction Model (DF-ECM) Approach - The Case of Developing Asia," Working Papers 565, Queen Mary University of London, School of Economics and Finance.
  9. Michael Beenstock & Daniel Felsenstein, 2010. "Spatial error correction and cointegration in nonstationary panel data: regional house prices in Israel," Journal of Geographical Systems, Springer, vol. 12(2), pages 189-206, June.
  10. You, Jing, 2013. "China's challenge for decarbonized growth: Forecasts from energy demand models," Journal of Policy Modeling, Elsevier, vol. 35(4), pages 652-668.
  11. Peter Robinson, 2008. "Developments in the analysis of spatial data," LSE Research Online Documents on Economics 25473, London School of Economics and Political Science, LSE Library.
  12. Auffhammer, Maximilian & Carson, Richard Taylor, 2004. "Forecasting the path of China's CO2 emissions using province level information," CUDARE Working Paper Series 0971, University of California at Berkeley, Department of Agricultural and Resource Economics and Policy, revised 2007.
  13. Pesaran, M. Hashem & Chudik, Alexander, 2011. "Aggregation in large dynamic panels," Globalization and Monetary Policy Institute Working Paper 101, Federal Reserve Bank of Dallas.
  14. Peter M Robinson, 2009. "Developments in the Analysis of Spatial Data," STICERD - Econometrics Paper Series 531, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
  15. Paelinck, J. & Mur, J. & Trívez, J., 2004. "Econometría espacial: más luces que sombras," Estudios de Economía Aplicada, Estudios de Economía Aplicada, vol. 22, pages 1-19, Diciembre.
  16. Espasa, Antoni & Mayo-Burgos, Iván, 2013. "Forecasting aggregates and disaggregates with common features," International Journal of Forecasting, Elsevier, vol. 29(4), pages 718-732.
  17. Cobb, Marcus P A, 2017. "Joint Forecast Combination of Macroeconomic Aggregates and Their Components," MPRA Paper 76556, University Library of Munich, Germany.
  18. Arnab Bhattacharjee & Chris Jensen-Butler, 2005. "Estimation of Spatial Weights Matrix in a Spatial Error Model, with an Application to Diffusion in Housing Demand," CRIEFF Discussion Papers 0519, Centre for Research into Industry, Enterprise, Finance and the Firm.
  19. Bhattacharjee, A. & Holly, S., 2010. "Understanding Interactions in Social Networks and Committees," Cambridge Working Papers in Economics 1003, Faculty of Economics, University of Cambridge.
  20. Xiaoxia Shi & Peter C. B. Phillips, 2010. "Nonlinear Cointegrating Regression under Weak Identification," Cowles Foundation Discussion Papers 1768, Cowles Foundation for Research in Economics, Yale University.
  21. Espasa, Antoni & Carlomagno, Guillermo, 2016. "Discovering common trends in a large set of disaggregates: statistical procedures and their properties," DES - Working Papers. Statistics and Econometrics. WS ws1519, Universidad Carlos III de Madrid. Departamento de Estadística.
  22. Nicholson, Alan, 2015. "Travel time reliability benefits: Allowing for correlation," Research in Transportation Economics, Elsevier, vol. 49(C), pages 14-21.
  23. Hampel, Katharina & Kunz, Marcus & Schanne, Norbert & Wapler, Rüdiger & Weyh, Antje, 2007. "Regional employment forecasts with spatial interdependencies," IAB Discussion Paper 200702, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
  24. Espasa, Antoni & Carlomagno, Guillermo, 2014. "The pairwise approach to model a large set of disaggregates with common trends," DES - Working Papers. Statistics and Econometrics. WS ws141309, Universidad Carlos III de Madrid. Departamento de Estadística.
  25. Raffaella Giacomini, 2014. "Economic theory and forecasting: lessons from the literature," CeMMAP working papers CWP41/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  26. Matías Mayor & Roberto Patuelli, 2012. "Short-Run Regional Forecasts: Spatial Models through Varying Cross-Sectional and Temporal Dimensions," Working Paper Series 15_12, The Rimini Centre for Economic Analysis, revised Oct 2012.
  27. M. Mucciardi & E. Otranto, 2016. "A Flexible Specification of Space–Time AutoRegressive Models," Working Paper CRENoS 201608, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
  28. Qin, Duo & Cagas, Marie Anne & Ducanes, Geoffrey & Magtibay-Ramos, Nedelyn & Quising, Pilipinas F., 2007. "Measuring Regional Market Integration in Developing Asia: a Dynamic Factor Error Correction Model (DF-ECM) Approach," Working Papers on Regional Economic Integration 8, Asian Development Bank.
  29. Trívez Bielsa, F.J., 2004. "Economía espacial: Una disciplina en auge," Estudios de Economía Aplicada, Estudios de Economía Aplicada, vol. 22, pages 1-18, Diciembre.
  30. Helena Marques & Gabriel Pino & Juan Dios Tena Horrillo, 2014. "Regional inflation dynamics using space–time models," Empirical Economics, Springer, vol. 47(3), pages 1147-1172, November.
  31. Blazej Mazur, 2015. "Density forecasts based on disaggregate data: nowcasting Polish inflation," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 15, pages 71-87.
  32. Baltagi, Badi H., 2006. "Forecasting with panel data," Discussion Paper Series 1: Economic Studies 2006,25, Deutsche Bundesbank, Research Centre.
  33. Espasa, Antoni & Carlomagno, Guillermo, 2015. "Forecasting a large set of disaggregates with common trends and outliers," DES - Working Papers. Statistics and Econometrics. WS ws1518, Universidad Carlos III de Madrid. Departamento de Estadística.
  34. Arnab Bhattacharjee & Eduardo Castro & João Marques, 2012. "Spatial Interactions in Hedonic Pricing Models: The Urban Housing Market of Aveiro, Portugal," Spatial Economic Analysis, Taylor & Francis Journals, vol. 7(1), pages 133-167, March.
  35. Rubén Hernández-Murillo & Michael T. Owyang, 2004. "The information content of regional employment data for forecasting aggregate conditions," Working Papers 2004-005, Federal Reserve Bank of St. Louis.
  36. Patrick Doupe, 2014. "The Costs of Error in Setting Reference Rates for Reduced Deforestation," CCEP Working Papers 1415, Centre for Climate Economics & Policy, Crawford School of Public Policy, The Australian National University.
  37. Kamarianakis, Yiannis & Prastacos, Poulicos, 2002. "Space-time modeling of traffic flow," ERSA conference papers ersa02p141, European Regional Science Association.
  38. Massimiliano Agovino & Antonio Garofalo, 2013. "Dipendenza spaziale contemporanea e non contemporanea nei tassi di disoccupazione: un tentativo di analisi empirica dei dati provinciali italiani," RIVISTA DI ECONOMIA E STATISTICA DEL TERRITORIO, FrancoAngeli Editore, vol. 2013(3), pages 45-82.
  39. Girum D. Abate & Niels Haldrup, 2015. "Space-time modeling of electricity spot prices," CREATES Research Papers 2015-22, Department of Economics and Business Economics, Aarhus University.
  40. Yu Hao & Zong-Yong Zhang & Hua Liao & Yi-Ming Wei, 2014. "China's Farewell to Coal: A Forecast of Coal Consumption through 2020," CEEP-BIT Working Papers 76, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.
  41. Bhattacharjee, Arnab & Jensen-Butler, Chris, 2011. "Estimation of the Spatial Weights Matrix under Structural Constraints," SIRE Discussion Papers 2011-48, Scottish Institute for Research in Economics (SIRE).
  42. Alberto Baffigi & Roberto Golinelli & Giuseppe Parigi, 2002. "Real-time GDP forecasting in the euro area," Temi di discussione (Economic working papers) 456, Bank of Italy, Economic Research and International Relations Area.
  43. Kelejian, Harry H. & Prucha, Ingmar R., 2004. "Estimation of simultaneous systems of spatially interrelated cross sectional equations," Journal of Econometrics, Elsevier, vol. 118(1-2), pages 27-50.
  44. Caporin Massimiliano & Paruolo Paolo, 2005. "Spatial effects in multivariate ARCH," Economics and Quantitative Methods qf0501, Department of Economics, University of Insubria.
  45. Stratford M. Douglas & Julia N. Popova, 2011. "Econometric Estimation of Spatial Patterns in Electricity Prices," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 81-106.
  46. Doupe, Patrick, 2014. "The costs of error in setting reference rates for reduced deforestation," Working Papers 249497, Australian National University, Centre for Climate Economics & Policy.
  47. Frédérick Demers & Annie De Champlain, 2005. "Forecasting Core Inflation in Canada: Should We Forecast the Aggregate or the Components?," Staff Working Papers 05-44, Bank of Canada.
  48. Arnab Bhattacharjee & Chris Jensen-Butler, 2005. "A Model of Regional Housing Markets in England and Wales," CRIEFF Discussion Papers 0508, Centre for Research into Industry, Enterprise, Finance and the Firm.
  49. Carson, Richard T. & Cenesizoglu, Tolga & Parker, Roger, 2011. "Forecasting (aggregate) demand for US commercial air travel," International Journal of Forecasting, Elsevier, vol. 27(3), pages 923-941, July.
  50. Youri Davydov & Vygantas Paulauskas, 2008. "On estimation of parameters for spatial autoregressive model," Statistical Inference for Stochastic Processes, Springer, vol. 11(3), pages 237-247, October.
  51. Baltagi, Badi H., 2013. "Panel Data Forecasting," Handbook of Economic Forecasting, Elsevier.
  52. Pino, Gabriel & Espasa, Antoni & Tena, Juan de Dios, 2008. "Forecasting Spanish inflation using information from different sectors and geographical areas," DES - Working Papers. Statistics and Econometrics. WS ws080101, Universidad Carlos III de Madrid. Departamento de Estadística.
  53. 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.
  54. Espasa, Antoni & Tena, Juan de Dios & Pino, Gabriel, 2013. "Forecasting disaggregates by sectors and regions : the case of inflation in the euro area and Spain," DES - Working Papers. Statistics and Econometrics. WS ws130807, Universidad Carlos III de Madrid. Departamento de Estadística.
  55. Paulauskas, Vygantas, 2007. "On unit roots for spatial autoregressive models," Journal of Multivariate Analysis, Elsevier, vol. 98(1), pages 209-226, January.
This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.