IDEAS home Printed from https://ideas.repec.org/a/ris/apltrx/0296.html
   My bibliography  Save this article

Using spatial econometric models for regional unemployment forecasting

Author

Listed:
  • Semerikova, Elena

    (National Research University Higher School of Economics, Moscow, Russian Federation)

  • Demidova, Olga

    (National Research University Higher School of Economics, Moscow, Russian Federation)

Abstract

We consider forecasting unemployment in Russian and German region with the help of econometric panel data models. Using regional data from 2005 till 2012 we show that spatial panel data models perform better in terms of forecasting accuracy than other models (on average and at least for some distinct regions) such as non-spatial panel data models, pooled OLS, models without exploratory variables and naive forecasts (average value for one or several previous periods).

Suggested Citation

  • Semerikova, Elena & Demidova, Olga, 2016. "Using spatial econometric models for regional unemployment forecasting," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 43, pages 29-51.
    • Handle: RePEc:ris:apltrx:0296
    as

    Download full text from publisher

    File URL: http://pe.cemi.rssi.ru/pe_2016_43_029-051.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Aragon, Y. & Haughton, D. & Haughton, J. & Leconte, E. & Malin, E. & Ruiz-Gazen, A. & Thomas-Agnan. C., 1999. "Explaining the Pattern of Regional Unemployment: the Case of the Midi-Pyrenees Region," Papers 99.519, Toulouse - GREMAQ.
    2. 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.
    3. Schanne, N. & Wapler, R. & Weyh, A., 2010. "Regional unemployment forecasts with spatial interdependencies," International Journal of Forecasting, Elsevier, vol. 26(4), pages 908-926, October.
    4. Ron Martin, 1997. "Regional Unemployment Disparities and their Dynamics," Regional Studies, Taylor & Francis Journals, vol. 31(3), pages 237-252.
    5. Robert Lehmann & Klaus Wohlrabe, 2014. "Regional economic forecasting: state-of-the-art methodology and future challenges," Economics and Business Letters, Oviedo University Press, vol. 3(4), pages 218-231.
    6. Badi Baltagi & Dong Li, 2006. "Prediction in the Panel Data Model with Spatial Correlation: the Case of Liquor," Spatial Economic Analysis, Taylor & Francis Journals, vol. 1(2), pages 175-185.
    7. Franziska Lottmann, 2012. "Regional Unemployment in Germany: a spatial panel data analysis," ERSA conference papers ersa12p53, European Regional Science Association.
    8. Badi H. Baltagi & Dong Li, 2004. "Prediction in the Panel Data Model with Spatial Correlation," Advances in Spatial Science, in: Luc Anselin & Raymond J. G. M. Florax & Sergio J. Rey (ed.), Advances in Spatial Econometrics, chapter 13, pages 283-295, Springer.
    9. Konstantin Arkadievich Kholodilin & Boriss Siliverstovs & Stefan Kooths, 2008. "A Dynamic Panel Data Approach to the Forecasting of the GDP of German Länder," Spatial Economic Analysis, Taylor & Francis Journals, vol. 3(2), pages 195-207.
    10. repec:rim:rimwps:10-07 is not listed on IDEAS
    11. Eric Girardin & Konstantin A. Kholodilin, 2011. "How helpful are spatial effects in forecasting the growth of Chinese provinces?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(7), pages 622-643, November.
    12. 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.
    13. Taub, Allan J., 1979. "Prediction in the context of the variance-components model," Journal of Econometrics, Elsevier, vol. 10(1), pages 103-107, April.
    14. Lee, Lung-fei & Yu, Jihai, 2010. "Estimation of spatial autoregressive panel data models with fixed effects," Journal of Econometrics, Elsevier, vol. 154(2), pages 165-185, February.
    15. Greenwood, Michael J, et al, 1991. "Migration, Regional Equilibrium, and the Estimation of Compensating Differentials," American Economic Review, American Economic Association, vol. 81(5), pages 1382-1390, December.
    16. Stephen T. Marston, 1985. "Two Views of the Geographic Distribution of Unemployment," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 100(1), pages 57-79.
    17. J. Paul Elhorst, 2003. "The Mystery of Regional Unemployment Differentials: Theoretical and Empirical Explanations," Journal of Economic Surveys, Wiley Blackwell, vol. 17(5), pages 709-748, December.
    18. Simonetta Longhi & Peter Nijkamp, 2007. "Forecasting Regional Labor Market Developments under Spatial Autocorrelation," International Regional Science Review, , vol. 30(2), pages 100-119, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. M. E. Baskakova & V. N. Baskakov & E. A. Yanenko, 2022. "Medium-Term Forecast of Government Spending on the Unemployment Social Protection System in Russia in the Conditions of Economic Recession," Studies on Russian Economic Development, Springer, vol. 33(1), pages 45-54, February.
    2. Aistov, Andrey & Nikolaeva, Tatiana, 2019. "Tourism-led growth hypothesis," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 56, pages 5-24.
    3. Natalia Larionova & Julia Varlamova & Julia Kolesnikova, 2021. "Does Digitalization Reduce Electricity Consumption? Evidence from Spatial Analysis," International Journal of Energy Economics and Policy, Econjournals, vol. 11(2), pages 413-419.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Anna Gloria Billé & Alessio Tomelleri & Francesco Ravazzolo, 2023. "Forecasting regional GDPs: a comparison with spatial dynamic panel data models," Spatial Economic Analysis, Taylor & Francis Journals, vol. 18(4), pages 530-551, October.
    2. 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.
    3. Baltagi, Badi H. & Pirotte, Alain, 2014. "Prediction in a spatial nested error components panel data model," International Journal of Forecasting, Elsevier, vol. 30(3), pages 407-414.
    4. Baltagi, Badi H., 2013. "Panel Data Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 995-1024, Elsevier.
    5. 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.
    6. Robert Lehmann & Klaus Wohlrabe, 2014. "Regional economic forecasting: state-of-the-art methodology and future challenges," Economics and Business Letters, Oviedo University Press, vol. 3(4), pages 218-231.
    7. Wozniak Marcin, 2020. "Forecasting the unemployment rate over districts with the use of distinct methods," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(2), pages 1-20, April.
    8. Semerikova, Elena, 2014. "Unemployment in East and West Germany: Spatial panel data analysis," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 35(3), pages 107-132.
    9. Valerij Gamukin, 2017. "Structural Change of Gross Regional Product in the Subjects of Ural Federal District," Economy of region, Centre for Economic Security, Institute of Economics of Ural Branch of Russian Academy of Sciences, vol. 1(2), pages 410-421.
    10. 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.
    11. Ana Angulo & Jesús Mur & Javier Trivez, 2014. "Measure of the resilience to Spanish economic crisis: the role of specialization," Economics and Business Letters, Oviedo University Press, vol. 3(4), pages 263-275.
    12. Maria Francesca Cracolici & Miranda Cuffaro & Peter Nijkamp, 2007. "Geographical Distribution of Unemployment: An Analysis of Provincial Differences in Italy," Growth and Change, Wiley Blackwell, vol. 38(4), pages 649-670, December.
    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.
    14. Franziska Lottmann, 2012. "Regional Unemployment in Germany: a spatial panel data analysis," ERSA conference papers ersa12p53, European Regional Science Association.
    15. Iman Al‐Ayouty & Hoda Hassaballa, 2020. "Regional unemployment in Egypt: Spatial panel data analysis," African Development Review, African Development Bank, vol. 32(4), pages 565-577, December.
    16. Schanne, N. & Wapler, R. & Weyh, A., 2010. "Regional unemployment forecasts with spatial interdependencies," International Journal of Forecasting, Elsevier, vol. 26(4), pages 908-926, October.
    17. Jean-Sauveur Ay & Raja Chakir & Julie Le Gallo, 2014. "The effects of scale, space and time on the predictive accuracy of land use models," Working Papers 2014/02, INRA, Economie Publique.
    18. Franziska Lottmann, 2012. "Explaining regional unemployment differences in Germany: a spatial panel data analysis," SFB 649 Discussion Papers SFB649DP2012-026, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    19. Nina Vujanovic & Bruno Casella & Richard Bolwijn, . "Forecasting global FDI: a panel data approach," UNCTAD Transnational Corporations Journal, United Nations Conference on Trade and Development.
    20. Sarantis LOLOS & Evangelia PAPAPETROU, 2012. "Unemployment disparities and persistence Assessing the evidence from Greek regions, 1981-2008," Regional and Sectoral Economic Studies, Euro-American Association of Economic Development, vol. 12(1), pages 69-90.

    More about this item

    Keywords

    spatial panel data models; prediction; regional unemployment;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • R10 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ris:apltrx:0296. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Anatoly Peresetsky (email available below). General contact details of provider: http://appliedeconometrics.cemi.rssi.ru/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.