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Prediction Using Panel Data Regression with Spatial Random Effects

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  • Bernard Fingleton

Abstract

This paper considers some of the issues and difficulties relating to the use of spatial paneldata regression in prediction, illustrated by the effects of mass immigration on wages andincome levels in local authority areas of Great Britain. Motivated by contemporary urbaneconomics theory, and using recent advances in spatial econometrics, the panel regression haswages dependent on employment density and the efficiency of the labour force. There aretwo types of spatial interaction, a spatial lag of wages, and an autoregressive process for errorcomponents. The estimates suggest that increased employment densities will increase wagelevels, but wages may fall if migrants are under-qualified. This uncertainty highlights the factthat ex ante forecasting should be used with great caution as a basis for policy decisions.

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Bibliographic Info

Paper provided by Spatial Economics Research Centre, LSE in its series SERC Discussion Papers with number 0007.

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Date of creation: Sep 2008
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Handle: RePEc:cep:sercdp:0007

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Web page: http://www.spatialeconomics.ac.uk/SERC/publications/default.asp

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Keywords: panel data; spatially correlated error components; economic geography; spatialeconometrics;

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References

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  1. Luciano Gutierrez, 2006. "Panel Unit-root Tests for Cross-sectionally Correlated Panels: A Monte Carlo Comparison," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 68(4), pages 519-540, 08.
  2. Antonio Ciccone & Robert E. Hall, 1993. "Productivity and the Density of Economic Activity," NBER Working Papers 4313, National Bureau of Economic Research, Inc.
  3. Badi H. Baltagi & Dong Li, 2006. "Prediction in the Panel Data Model with Spatial Correlation: The Case of Liquor," Center for Policy Research Working Papers 84, Center for Policy Research, Maxwell School, Syracuse University.
  4. Banerjee, Anindya, 1999. " Panel Data Unit Roots and Cointegration: An Overview," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 61(0), pages 607-29, Special I.
  5. James P. LeSage & R. Kelley Pace, 2004. "Models for Spatially Dependent Missing Data," The Journal of Real Estate Finance and Economics, Springer, vol. 29(2), pages 233-254, 09.
  6. repec:cup:cbooks:9780521801386 is not listed on IDEAS
  7. John M. Quigley, 1998. "Urban Diversity and Economic Growth," Journal of Economic Perspectives, American Economic Association, vol. 12(2), pages 127-138, Spring.
  8. Banerjee, Anindya & Massimiliano Marcellino & Chiara Osbat, 2002. "Testing for PPP: Should We Use Panel Methods?," Royal Economic Society Annual Conference 2002 13, Royal Economic Society.
  9. Kapoor, Mudit & Kelejian, Harry H. & Prucha, Ingmar R., 2007. "Panel data models with spatially correlated error components," Journal of Econometrics, Elsevier, vol. 140(1), pages 97-130, September.
  10. Kelejian, Harry H. & Prucha, Ingmar R., 2007. "The relative efficiencies of various predictors in spatial econometric models containing spatial lags," Regional Science and Urban Economics, Elsevier, vol. 37(3), pages 363-374, May.
  11. Rivera-Batiz, Francisco L., 1988. "Increasing returns, monopolistic competition, and agglomeration economies in consumption and production," Regional Science and Urban Economics, Elsevier, vol. 18(1), pages 125-153, February.
  12. repec:cup:cbooks:9780521805247 is not listed on IDEAS
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Cited by:
  1. Badi H. Baltagi & Bernard Fingleton & Alain Pirotte, 2012. "Estimating and Forecasting With A Dynamic Spatial Panel Data Model," Center for Policy Research Working Papers 149, Center for Policy Research, Maxwell School, Syracuse University.
  2. Fingleton, Bernard, 2010. "Predicting the Geography of House Prices," MPRA Paper 21113, University Library of Munich, Germany.
  3. 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.

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