IDEAS home Printed from https://ideas.repec.org/p/een/camaaa/2007-17.html
   My bibliography  Save this paper

Six Leading Indexes Of New Zealand Employment

Author

Listed:
  • Edda Claus
  • Iris Claus

Abstract

This paper constructs six leading indexes of New Zealand employment and compares their short term forecasting performance. Forecasting New Zealand employment is particularly difficult owing to the volatility of the data and the short sample size of available time series. These restrictions make leading indexes especially appealing. The paper has two aims. The first is to construct an effective forecasting tool. The second is to evaluate leading indexes constructed using different methods available in the literature. The results show that an index constructed using the traditional NBER method dominates in terms of forecasting performance. The results also suggest that increasing the dataset does not strengthen the index and that exogenously determining the weights of component series can add to forecasting performance.

Suggested Citation

  • Edda Claus & Iris Claus, 2007. "Six Leading Indexes Of New Zealand Employment," CAMA Working Papers 2007-17, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    • Handle: RePEc:een:camaaa:2007-17
    as

    Download full text from publisher

    File URL: https://cama.crawford.anu.edu.au/sites/default/files/publication/cama_crawford_anu_edu_au/2021-06/17_claus_claus_2007.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Edda Claus & Iris Claus, 2002. "How many jobs? A leading indicator model of New Zealand employment," Treasury Working Paper Series 02/13, New Zealand Treasury.
    2. Edda Claus, 2011. "Seven Leading Indexes of New Zealand Employment," The Economic Record, The Economic Society of Australia, vol. 87(276), pages 76-89, March.
    3. Edda Claus, "undated". "Constructing NEO: A Near-term Employment Outlook," Working Papers-Department of Finance Canada 2001-07, Department of Finance Canada.
    4. Harding, Don & Pagan, Adrian, 2002. "Dissecting the cycle: a methodological investigation," Journal of Monetary Economics, Elsevier, vol. 49(2), pages 365-381, March.
    5. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
    6. James H. Stock & Mark W. Watson, 1989. "New Indexes of Coincident and Leading Economic Indicators," NBER Chapters, in: NBER Macroeconomics Annual 1989, Volume 4, pages 351-409, National Bureau of Economic Research, Inc.
    7. 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.
    Full references (including those not matched with items 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. Sonia de Lucas Santos & M. Jesús Delgado Rodríguez & Inmaculada Álvarez Ayuso & José Luis Cendejas Bueno, 2011. "Los ciclos económicos internacionales: antecedentes y revisión de la literatura," Cuadernos de Economía - Spanish Journal of Economics and Finance, Asociación Cuadernos de Economía, vol. 34(95), pages 73-84, Agosto.
    2. Andrea Carriero & Francesco Corsello & Massimiliano Marcellino, 2022. "The global component of inflation volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(4), pages 700-721, June.
    3. Brandyn Bok & Daniele Caratelli & Domenico Giannone & Argia M. Sbordone & Andrea Tambalotti, 2018. "Macroeconomic Nowcasting and Forecasting with Big Data," Annual Review of Economics, Annual Reviews, vol. 10(1), pages 615-643, August.
    4. Ma, Tao & Zhou, Zhou & Antoniou, Constantinos, 2018. "Dynamic factor model for network traffic state forecast," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 281-317.
    5. Jonas Krampe & Luca Margaritella, 2021. "Factor Models with Sparse VAR Idiosyncratic Components," Papers 2112.07149, arXiv.org, revised May 2022.
    6. Olivier Darné & Laurent Ferrara, 2011. "Identification of Slowdowns and Accelerations for the Euro Area Economy," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 73(3), pages 335-364, June.
    7. Aiolfi, Marco & Catão, Luis A.V. & Timmermann, Allan, 2011. "Common factors in Latin America's business cycles," Journal of Development Economics, Elsevier, vol. 95(2), pages 212-228, July.
    8. Cubadda, Gianluca & Guardabascio, Barbara & Hecq, Alain, 2013. "A general to specific approach for constructing composite business cycle indicators," Economic Modelling, Elsevier, vol. 33(C), pages 367-374.
    9. Fabio Canova & Matteo Ciccarelli, 2009. "Estimating Multicountry Var Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 50(3), pages 929-959, August.
    10. Rua, António, 2017. "A wavelet-based multivariate multiscale approach for forecasting," International Journal of Forecasting, Elsevier, vol. 33(3), pages 581-590.
    11. Santos, Sonia de Lucas & Rodríguez, María Jesús Delgado & Ayuso, Inmaculada Álvarez, 2011. "Application of factor models for the identification of countries sharing international reference-cycles," Economic Modelling, Elsevier, vol. 28(6), pages 2424-2431.
    12. Hwee Kwan Chow & Keen Meng Choy, 2009. "Analyzing and forecasting business cycles in a small open economy: A dynamic factor model for Singapore," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2009(1), pages 19-41.
    13. Bettina Becker & Stephen G. Hall, 2009. "A new look at economic convergence in Europe: a common factor approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 14(1), pages 85-97.
    14. Stock, J.H. & Watson, M.W., 2016. "Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 415-525, Elsevier.
    15. William A. Barnett & Biyan Tang, 2016. "Chinese Divisia Monetary Index and GDP Nowcasting," Open Economies Review, Springer, vol. 27(5), pages 825-849, November.
    16. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    17. Issler, Joao Victor & Notini, Hilton & Rodrigues, Claudia & Soares, Ana Flávia, 2013. "Constructing coincident indices of economic activity for the Latin American economy," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 67(1), April.
    18. Simon Beyeler & Sylvia Kaufmann, 2021. "Reduced‐form factor augmented VAR—Exploiting sparsity to include meaningful factors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(7), pages 989-1012, November.
    19. Semih Emre Cekin & Menelik S. Geremew & Hardik Marfatia, 2019. "Monetary policy co-movement and spillover of shocks among BRICS economies," Applied Economics Letters, Taylor & Francis Journals, vol. 26(15), pages 1253-1263, September.
    20. Abdić Ademir & Resić Emina & Abdić Adem, 2020. "Modelling and forecasting GDP using factor model: An empirical study from Bosnia and Herzegovina," Croatian Review of Economic, Business and Social Statistics, Sciendo, vol. 6(1), pages 10-26, May.

    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:een:camaaa:2007-17. 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: Cama Admin (email available below). General contact details of provider: https://edirc.repec.org/data/asanuau.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.