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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, Centre for Economic Performance, LSE.
    • Handle: RePEc:cep:sercdp:0007
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    8. Rebecca Riley & Martin Weale, 2006. "Commentary: Immigration and Its Effects," National Institute Economic Review, National Institute of Economic and Social Research, vol. 198(1), pages 4-9, October.
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    Cited by:

    1. 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.
    2. 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.
    3. Matías Mayor & Roberto Patuelli, 2012. "Short-Run Regional Forecasts: Spatial Models through Varying Cross-Sectional and Temporal Dimensions," Advances in Spatial Science, in: Esteban Fernández Vázquez & Fernando Rubiera Morollón (ed.), Defining the Spatial Scale in Modern Regional Analysis, edition 127, chapter 0, pages 173-192, Springer.
    4. Giuseppe Arbia, 2011. "A Lustrum of SEA: Recent Research Trends Following the Creation of the Spatial Econometrics Association (2007--2011)," Spatial Economic Analysis, Taylor & Francis Journals, vol. 6(4), pages 377-395, July.
    5. Fingleton, Bernard, 2010. "Predicting the geography of house prices," LSE Research Online Documents on Economics 33507, London School of Economics and Political Science, LSE Library.
    6. Luc Anselin, 2010. "Thirty years of spatial econometrics," Papers in Regional Science, Wiley Blackwell, vol. 89(1), pages 3-25, March.
    7. repec:unu:wpaper:wp2012-74 is not listed on IDEAS
    8. Yongfu Huang & Jingjing He, 2012. "The Decarbonization of China's Agriculture," WIDER Working Paper Series wp-2012-074, World Institute for Development Economic Research (UNU-WIDER).
    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. He, Jingjing & Huang, Yongfu, 2012. "The Decarbonization of China's Agriculture," WIDER Working Paper Series 074, World Institute for Development Economic Research (UNU-WIDER).
    11. repec:asg:wpaper:1013 is not listed on IDEAS
    12. Fingleton, Bernard & Palombi, Silvia, 2013. "Spatial panel data estimation, counterfactual predictions, and local economic resilience among British towns in the Victorian era," Regional Science and Urban Economics, Elsevier, vol. 43(4), pages 649-660.
    13. 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.

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    More about this item

    Keywords

    panel data; spatially correlated error components; economic geography; spatial econometrics;
    All these keywords.

    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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