IDEAS home Printed from https://ideas.repec.org/a/taf/emetrv/v29y2010i5-6p511-533.html
   My bibliography  Save this article

Bagging or Combining (or Both)? An Analysis Based on Forecasting U.S. Employment Growth

Author

Listed:
  • David Rapach
  • Jack Strauss

Abstract

Forecasting a macroeconomic variable is challenging in an environment with many potential predictors whose predictive ability can vary over time. We compare two approaches to forecasting U.S. employment growth in this type of environment. The first approach applies bootstrap aggregating (bagging) to a general-to-specific procedure based on a general dynamic linear regression model with 30 potential predictors. The second approach considers several methods for combining forecasts from 30 individual autoregressive distributed lag (ARDL) models, where each individual ARDL model contains a potential predictor. We analyze bagging and combination forecasts at multiple horizons over four different out-of-sample periods using a mean square forecast error (MSFE) criterion and forecast encompassing tests. We find that bagging forecasts often deliver the lowest MSFE. Interestingly, we also find that incorporating information from both bagging and combination forecasts based on principal components often leads to further gains in forecast accuracy.

Suggested Citation

  • David Rapach & Jack Strauss, 2010. "Bagging or Combining (or Both)? An Analysis Based on Forecasting U.S. Employment Growth," Econometric Reviews, Taylor & Francis Journals, vol. 29(5-6), pages 511-533.
  • Handle: RePEc:taf:emetrv:v:29:y:2010:i:5-6:p:511-533
    DOI: 10.1080/07474938.2010.481550
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1080/07474938.2010.481550
    Download Restriction: Access to full text is restricted to subscribers.

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. Aye, Goodness C. & Balcilar, Mehmet & Gupta, Rangan & Majumdar, Anandamayee, 2015. "Forecasting aggregate retail sales: The case of South Africa," International Journal of Production Economics, Elsevier, vol. 160(C), pages 66-79.
    2. Jordan, Steven J. & Vivian, Andrew & Wohar, Mark E., 2017. "Forecasting market returns: bagging or combining?," International Journal of Forecasting, Elsevier, vol. 33(1), pages 102-120.
    3. Rangan Gupta & Mampho P. Modise & Josine Uwilingiye, 2016. "Out-of-Sample Equity Premium Predictability in South Africa: Evidence from a Large Number of Predictors," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 52(8), pages 1935-1955, August.
    4. Rangan Gupta & Alain Kabundi & Stephen Miller & Josine Uwilingiye, 2014. "Using large data sets to forecast sectoral employment," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(2), pages 229-264, June.
    5. Huiyu Huang & Tae-Hwy Lee, 2013. "Forecasting Value-at-Risk Using High-Frequency Information," Econometrics, MDPI, Open Access Journal, vol. 1(1), pages 1-14, June.
    6. Kopoin, Alexandre & Moran, Kevin & Paré, Jean-Pierre, 2013. "Forecasting regional GDP with factor models: How useful are national and international data?," Economics Letters, Elsevier, vol. 121(2), pages 267-270.
    7. Burcu Gurcihan Yunculer & Gonul Sengul & Arzu Yavuz, 2014. "A Quest for Leading Indicators of the Turkish Unemployment Rate," Central Bank Review, Research and Monetary Policy Department, Central Bank of the Republic of Turkey, vol. 14(1), pages 23-45.
    8. Strauss, Jack, 2013. "Does housing drive state-level job growth? Building permits and consumer expectations forecast a state’s economic activity," Journal of Urban Economics, Elsevier, vol. 73(1), pages 77-93.
    9. Rapach, David E. & Strauss, Jack K., 2012. "Forecasting US state-level employment growth: An amalgamation approach," International Journal of Forecasting, Elsevier, vol. 28(2), pages 315-327.
    10. repec:eee:reveco:v:49:y:2017:i:c:p:276-291 is not listed on IDEAS
    11. Karol Szafranek, 2017. "Bagged artificial neural networks in forecasting inflation: An extensive comparison with current modelling frameworks," NBP Working Papers 262, Narodowy Bank Polski, Economic Research Department.
    12. Jordan, Steven J. & Vivian, Andrew & Wohar, Mark E., 2016. "Can commodity returns forecast Canadian sector stock returns?," International Review of Economics & Finance, Elsevier, vol. 41(C), pages 172-188.
    13. repec:eee:intfor:v:33:y:2017:i:4:p:894-914 is not listed on IDEAS

    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:taf:emetrv:v:29:y:2010:i:5-6:p:511-533. 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: (). General contact details of provider: http://www.tandfonline.com/LECR20 .

    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.

    We have no references for this item. You can help adding them by using 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.