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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 panel data regression in prediction, illustrated by the effects of mass immigration on wages and income levels in local authority areas of Great Britain. Motivated by contemporary urban economics theory, and using recent advances in spatial econometrics, the panel regression has wages dependent on employment density and the efficiency of the labour force. There are two types of spatial interaction, a spatial lag of wages, and an autoregressive process for error components. The estimates suggest that increased employment densities will increase wage levels, but wages may fall if migrants are under-qualified. This uncertainty highlights the fact that ex ante forecasting should be used with great caution as a basis for policy decisions.

Suggested Citation

  • Bernard Fingleton, 2008. "Prediction Using Panel Data Regression with Spatial Random Effects," SERC Discussion Papers 0007, Spatial Economics Research Centre, LSE.
  • Handle: RePEc:cep:sercdp:0007
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    References listed on IDEAS

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    1. Ciccone, Antonio & Hall, Robert E, 1996. "Productivity and the Density of Economic Activity," American Economic Review, American Economic Association, vol. 86(1), pages 54-70, March.
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    5. 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, August.
    6. Anindya Banerjee & Massimiliano Marcellino & Chiara Osbat, 2005. "Testing for PPP: Should we use panel methods?," Empirical Economics, Springer, vol. 30(1), pages 77-91, January.
    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. 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.
    9. 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 607-629, Special I.
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    Citations

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    Cited by:

    1. 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.
    2. He, Jingjing & Huang, Yongfu, 2012. "The Decarbonization of China's Agriculture," WIDER Working Paper Series 074, World Institute for Development Economic Research (UNU-WIDER).
    3. Fingleton, Bernard, 2010. "Predicting the Geography of House Prices," MPRA Paper 21113, University Library of Munich, Germany.
    4. 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.
    5. Matías Mayor & Roberto Patuelli, 2012. "Short-Run Regional Forecasts: Spatial Models through Varying Cross-Sectional and Temporal Dimensions," Working Paper series 15_12, Rimini Centre for Economic Analysis, revised Oct 2012.
    6. Luc Anselin, 2010. "Thirty years of spatial econometrics," Papers in Regional Science, Wiley Blackwell, vol. 89(1), pages 3-25, March.

    More about this item

    Keywords

    panel data; spatially correlated error components; economic geography; spatial econometrics;

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • O10 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - General
    • F02 - International Economics - - General - - - International Economic Order and Integration

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