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Prediction Using Panel Data Regression with Spatial Random Effects

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  • Bernard Fingleton

    (Department of Economics, University of Strathclyde, Glasgow, UK, bf100@cam.ac.uk)

Abstract

This article 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 labor 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 underqualified. 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, 2009. "Prediction Using Panel Data Regression with Spatial Random Effects," International Regional Science Review, , vol. 32(2), pages 195-220, April.
  • Handle: RePEc:sae:inrsre:v:32:y:2009:i:2:p:195-220
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    References listed on IDEAS

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    1. 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, September.
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    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. 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.
    8. Francisco L. Rivera-Batiz & Luis A. Rivera-Batiz, 2018. "Increasing Returns, Monopolistic Competition, and Agglomeration Economies in Consumption and Production," World Scientific Book Chapters,in: International Trade, Capital Flows and Economic Development, chapter 6, pages 141-176 World Scientific Publishing Co. Pte. Ltd..
    9. 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.
    10. 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.
    11. 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. 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.
    2. 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.
    3. 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.
    4. Luc Anselin, 2010. "Thirty years of spatial econometrics," Papers in Regional Science, Wiley Blackwell, vol. 89(1), pages 3-25, March.
    5. 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.
    6. He, Jingjing & Huang, Yongfu, 2012. "The Decarbonization of China's Agriculture," WIDER Working Paper Series 074, World Institute for Development Economic Research (UNU-WIDER).
    7. Baltagi, Badi H. & Fingleton, Bernard & Pirotte, Alain, 2018. "A Time-Space Dynamic Panel Data Model with Spatial Moving Average Errors," MPRA Paper 86371, University Library of Munich, Germany.
    8. Baltagi, Badi H. & Fingleton, Bernard & Pirotte, Alain, 2018. "A Time-Space Dynamic Panel Data Model with Spatial Moving Average Errors," IZA Discussion Papers 11587, Institute for the Study of Labor (IZA).

    More about this item

    Keywords

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

    JEL classification:

    • J1 - Labor and Demographic Economics - - Demographic Economics

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