IDEAS home Printed from https://ideas.repec.org/p/nzb/nzbans/2016-04.html
   My bibliography  Save this paper

Developing a labour utilisation composite index for New Zealand

Author

Listed:

Abstract

This Note presents a labour utilisation composite index (LUCI) for the New Zealand economy. We use principal component analysis to extract the underlying movements from a set of seventeen labour market variables. The LUCI fits the New Zealand business cycle well, and is particularly useful in situations when different labour market variables give contradictory signals, or when individual labour market variables have idiosyncratic movements.

Suggested Citation

  • Jed Armstrong & Günes Kamber & Özer Karagedikli, 2016. "Developing a labour utilisation composite index for New Zealand," Reserve Bank of New Zealand Analytical Notes series AN2016/04, Reserve Bank of New Zealand.
  • Handle: RePEc:nzb:nzbans:2016/04
    as

    Download full text from publisher

    File URL: http://rbnz.govt.nz/-/media/ReserveBank/Files/Publications/Analytical%20notes/2016/an2016-04.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nadezhda Malysheva & Pierre-Daniel G. Sarte, 2009. "Heterogeneity in sectoral employment and the business cycle," Economic Quarterly, Federal Reserve Bank of Richmond, vol. 95(Fall), pages 335-355.
    2. Hess T. Chung & Bruce Fallick & Christopher J. Nekarda & David Ratner, 2014. "Assessing the Change in Labor Market Conditions," FEDS Notes 2014-05-22, Board of Governors of the Federal Reserve System (U.S.).
    3. Viv B. Hall & C. John McDermott, 2011. "A quarterly post-Second World War real GDP series for New Zealand," New Zealand Economic Papers, Taylor & Francis Journals, vol. 45(3), pages 273-298, March.
    4. Brian Silverstone & Will Bell, 2011. "Gross Labour Market Flows in New Zealand: Some Questions and Answers," Working Papers in Economics 11/15, University of Waikato.
    5. Craig S. Hakkio & Jonathan L. Willis, 2013. "Assessing labor market conditions: the level of activity and the speed of improvement," Macro Bulletin, Federal Reserve Bank of Kansas City, issue july18, pages 1-2, July.
    6. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
    7. Nikolay Gospodinov & Serena Ng, 2013. "Commodity Prices, Convenience Yields, and Inflation," The Review of Economics and Statistics, MIT Press, vol. 95(1), pages 206-219, March.
    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. Martin Hodula & Simona Malovana & Jan Frait, 2019. "Introducing a New Index of Households' Macroeconomic Conditions," Working Papers 2019/10, Czech National Bank.
    2. repec:cnb:ocpubv:as21 is not listed on IDEAS

    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. repec:dau:papers:123456789/11663 is not listed on IDEAS
    2. repec:ipg:wpaper:19 is not listed on IDEAS
    3. repec:dau:papers:123456789/11692 is not listed on IDEAS
    4. Simona Delle Chiaie & Laurent Ferrara & Domenico Giannone, 2022. "Common factors of commodity prices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(3), pages 461-476, April.
    5. Yannick Le Pen & Benoît Sévi, 2013. "Futures Trading and the Excess Comovement of Commodity Prices," Working Papers halshs-00793724, HAL.
    6. Gonçalves, Sílvia & Perron, Benoit, 2020. "Bootstrapping factor models with cross sectional dependence," Journal of Econometrics, Elsevier, vol. 218(2), pages 476-495.
    7. Gonçalves, Sílvia & Perron, Benoit, 2014. "Bootstrapping factor-augmented regression models," Journal of Econometrics, Elsevier, vol. 182(1), pages 156-173.
    8. repec:cte:wsrepe:23974 is not listed on IDEAS
    9. repec:ipg:wpaper:2013-019 is not listed on IDEAS
    10. Albuquerque, Bruno & Baumann, Ursel, 2017. "Will US inflation awake from the dead? The role of slack and non-linearities in the Phillips curve," Journal of Policy Modeling, Elsevier, vol. 39(2), pages 247-271.
    11. repec:ipg:wpaper:2014-414 is not listed on IDEAS
    12. Massimo Guidolin & Manuela Pedio, 2020. "Distilling Large Information Sets to Forecast Commodity Returns: Automatic Variable Selection or HiddenMarkov Models?," BAFFI CAREFIN Working Papers 20140, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    13. repec:dau:papers:123456789/6800 is not listed on IDEAS
    14. Simona Malovaná & Martin Hodula & Jan Frait, 2021. "What Does Really Drive Consumer Confidence?," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 155(3), pages 885-913, June.
    15. Derek Bunn, Julien Chevallier, Yannick Le Pen, and Benoit Sevi, 2017. "Fundamental and Financial Influences on the Co-movement of Oil and Gas Prices," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
    16. repec:dau:papers:123456789/11382 is not listed on IDEAS
    17. Juan José Echavarría & Andrés González, 2012. "Choques internacionales reales y financieros y su impacto sobre la economía colombiana," Revista ESPE - Ensayos sobre Política Económica, Banco de la Republica de Colombia, vol. 30(69), pages 14-66, December.
    18. Chen, Qitong & Hong, Yongmiao & Li, Haiqi, 2024. "Time-varying forecast combination for factor-augmented regressions with smooth structural changes," Journal of Econometrics, Elsevier, vol. 240(1).
    19. Hertrich Markus, 2019. "A Novel Housing Price Misalignment Indicator for Germany," German Economic Review, De Gruyter, vol. 20(4), pages 759-794, December.
    20. Kim, Hyun Hak & Swanson, Norman R., 2018. "Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods," International Journal of Forecasting, Elsevier, vol. 34(2), pages 339-354.
    21. David Havrlant & Peter Tóth & Julia Wörz, 2016. "On the optimal number of indicators – nowcasting GDP growth in CESEE," Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue 4, pages 54-72.
    22. Alain-Philippe Fortin & Patrick Gagliardini & O. Scaillet, 2022. "Eigenvalue tests for the number of latent factors in short panels," Swiss Finance Institute Research Paper Series 22-81, Swiss Finance Institute.
    23. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
    24. Fan, Jianqing & Liao, Yuan & Shi, Xiaofeng, 2015. "Risks of large portfolios," Journal of Econometrics, Elsevier, vol. 186(2), pages 367-387.
    25. İshak Demi̇r & Burak A. Eroğlu & Seçi̇l Yildirim‐Karaman, 2022. "Heterogeneous Effects of Unconventional Monetary Policy on the Bond Yields across the Euro Area," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 54(5), pages 1425-1457, August.
    26. 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.
    27. Norman R. Swanson & Nii Ayi Armah, 2011. "Diffusion Index Models and Index Proxies: Recent Results and New Directions," Departmental Working Papers 201114, Rutgers University, Department of Economics.
    28. Yuefeng Han & Rong Chen & Dan Yang & Cun-Hui Zhang, 2020. "Tensor Factor Model Estimation by Iterative Projection," Papers 2006.02611, arXiv.org, revised Jul 2024.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:nzb:nzbans:2016/04. 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: Reserve Bank of New Zealand Knowledge Centre (email available below). General contact details of provider: https://edirc.repec.org/data/rbngvnz.html .

    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.