IDEAS home Printed from https://ideas.repec.org/a/rre/publsh/v37y2007i1p64-81.html
   My bibliography  Save this article

A Rank-Order Test on the Statistical Performance of Neural Network Models for Regional Labor Market Forecasts

Author

Listed:
  • Patuelli, Roberto

    (Free U Amsterdam)

  • Longhi, Simonetta

    (ISER, U Essex)

  • Reggiani, Aura

    (U Bologna)

  • Nijkamp, Peter

    (Free U Amsterdam)

  • Blien, Uwe

    (Institut fur Arbeitsmarkt und Berufsforschung, Nuremburg)

Abstract

Using a panel of 439 German regions, we evaluate and compare the performance of various Neural Network (NN) models as forecasting tools for regional employment growth. Because of relevant differences in data availability between the former East and West Germany, the NN models are computed separately for the two parts of the country. The comparisons of the models and their ex post forecasts are carried out by means of a non-parametric test: viz. the Friedman statistic. The Friedman statistic tests the consistency of model results obtained in terms of their rank order. Since there is no normal distribution assumption, this methodology is an interesting substitute for a standard analysis of variance.

Suggested Citation

  • Patuelli, Roberto & Longhi, Simonetta & Reggiani, Aura & Nijkamp, Peter & Blien, Uwe, 2007. "A Rank-Order Test on the Statistical Performance of Neural Network Models for Regional Labor Market Forecasts," The Review of Regional Studies, Southern Regional Science Association, vol. 37(1), pages 64-81.
  • Handle: RePEc:rre:publsh:v:37:y:2007:i:1:p:64-81
    as

    Download full text from publisher

    File URL: http://journal.srsa.org/ojs/index.php/RRS/article/view/137/88
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Profit, Stefan & Tschernig, Rolf, 1998. "Germany's labor market problems: What to do and what not to do? A survey among experts," SFB 373 Discussion Papers 1998,94, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    2. Antonino Scarelli & Lorenzo Venzi, 1997. "Nonparametric Statistics In Multicriteria Analysis," Theory and Decision, Springer, vol. 43(1), pages 89-105, July.
    3. Simonetta Longhi & Peter Nijkamp & Aura Reggianni & Erich Maierhofer, 2005. "Neural Network Modeling as a Tool for Forecasting Regional Employment Patterns," International Regional Science Review, , vol. 28(3), pages 330-346, July.
    4. Frees, Edward W., 1995. "Assessing cross-sectional correlation in panel data," Journal of Econometrics, Elsevier, vol. 69(2), pages 393-414, October.
    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. repec:ura:ecregj:v:1:y:2017:i:2:p:410-421 is not listed on IDEAS
    2. 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.

    More about this item

    Keywords

    Forecast; Forecasting; Neural Networks; Neural; Regional Labor Markets; Regional; Regions;

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)
    • R15 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Econometric and Input-Output Models; Other Methods
    • R23 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Regional Migration; Regional Labor Markets; Population

    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:rre:publsh:v:37:y:2007:i:1:p:64-81. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher Yencha). General contact details of provider: http://www.srsa.org .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.