IDEAS home Printed from https://ideas.repec.org/p/mtu/wpaper/07_13.html
   My bibliography  Save this paper

A Quarterly Post-World War II Real GDP Series for New Zealand

Author

Listed:
  • Viv Hall

    (Victoria University of Wellington)

  • John McDermott

    (Reserve Bank of New Zealand)

Abstract

There are no official quarterly real GDP estimates for New Zealand for the period prior to 1977. We develop a seasonally adjusted series for 1947q2 to 2006q2, by linking quarterly observations from two recent official series to temporally disaggregated observations for an earlier time period. Annual real GDP series are disaggregated, using the information from two quarterly diffusion indexes, developed by Haywood and Campbell (1976). Three econometric models are used: the Chow and Lin (1971) model that disaggregates the level of GDP, and the Fernández (1981) and Litterman (1983) models that disaggregate changes in GDP. Statistical properties of the series are evaluated, and movements in the new series are benchmarked against qualitative research findings from New Zealand's post-WWII economic history. Our preferred quarterly series is based on results generated from the Chow-Lin model.

Suggested Citation

  • Viv Hall & John McDermott, 2007. "A Quarterly Post-World War II Real GDP Series for New Zealand," Working Papers 07_13, Motu Economic and Public Policy Research.
  • Handle: RePEc:mtu:wpaper:07_13
    as

    Download full text from publisher

    File URL: https://motu-www.motu.org.nz/wpapers/07_13.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Silver, J Lew, 1986. "Two Results Useful for Implementing Litterman's Procedure for Interpolating a Time Series [A Random Walk, Markov Model for the Distribution of Time Series]," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 129-130, January.
    2. Stock, James H. & Watson, Mark W., 1999. "Business cycle fluctuations in us macroeconomic time series," Handbook of Macroeconomics, in: J. B. Taylor & M. Woodford (ed.), Handbook of Macroeconomics, edition 1, volume 1, chapter 1, pages 3-64, Elsevier.
    3. Andrews, Donald W K & Ploberger, Werner, 1994. "Optimal Tests When a Nuisance Parameter Is Present Only under the Alternative," Econometrica, Econometric Society, vol. 62(6), pages 1383-1414, November.
    4. Litterman, Robert B, 1983. "A Random Walk, Markov Model for the Distribution of Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(2), pages 169-173, April.
    5. Chow, Gregory C & Lin, An-loh, 1971. "Best Linear Unbiased Interpolation, Distribution, and Extrapolation of Time Series by Related Series," The Review of Economics and Statistics, MIT Press, vol. 53(4), pages 372-375, November.
    6. Fernandez, Roque B, 1981. "A Methodological Note on the Estimation of Time Series," The Review of Economics and Statistics, MIT Press, vol. 63(3), pages 471-476, August.
    7. Tommaso Proietti, 2006. "Temporal disaggregation by state space methods: Dynamic regression methods revisited," Econometrics Journal, Royal Economic Society, vol. 9(3), pages 357-372, November.
    8. Andrew Harvey & Chia‐Hui Chung, 2000. "Estimating the underlying change in unemployment in the UK," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 163(3), pages 303-309.
    9. G. R. Hawke, 1975. "Income Estimation From Monetary Data: Further Explorations," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 21(3), pages 301-307, September.
    10. Andrews, Donald W K, 1993. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Econometrica, Econometric Society, vol. 61(4), pages 821-856, July.
    11. Hall, Viv B. & McDermott, C. John, 2009. "The New Zealand Business Cycle," Econometric Theory, Cambridge University Press, vol. 25(4), pages 1050-1069, August.
    12. Filippo Moauro & Giovanni Savio, 2005. "Temporal disaggregation using multivariate structural time series models," Econometrics Journal, Royal Economic Society, vol. 8(2), pages 214-234, July.
    13. Hawke, G R, 1975. "Income Estimation from Monetary Data: Further Explorations," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 21(3), pages 301-307, September.
    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. Vosen, Simeon & Schmidt, Torsten, 2012. "A monthly consumption indicator for Germany based on Internet search query data," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 19(7), pages 683-687.
    2. Willie Lahari & Alfred A. Haug & Arlene Garces-Ozanne, 2011. "Estimating Quarterly Gdp Data For The South Pacific Island Nations," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 56(01), pages 97-112.
    3. repec:zbw:rwirep:0208 is not listed on IDEAS
    4. Torsten Schmidt & Simeon Vosen, 2010. "A monthly consumption indicator for Germany based on internet search query data," Ruhr Economic Papers 0208, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.

    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. Cecilia Frale, "undated". "Do Surveys Help in Macroeconomic Variables Disaggregation and Estimation?," Working Papers wp2008-2, Department of the Treasury, Ministry of the Economy and of Finance.
    2. Marcellino, Massimiliano & Proietti, Tommaso & Frale, Cecilia & Mazzi, Gian Luigi, 2008. "A Monthly Indicator of the Euro Area GDP," CEPR Discussion Papers 7007, C.E.P.R. Discussion Papers.
    3. Tommaso Proietti, 2006. "Temporal disaggregation by state space methods: Dynamic regression methods revisited," Econometrics Journal, Royal Economic Society, vol. 9(3), pages 357-372, November.
    4. Laura Bisio & Filippo Moauro, 2018. "Temporal disaggregation by dynamic regressions: Recent developments in Italian quarterly national accounts," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(4), pages 471-494, November.
    5. Cecilia Frale & Massimiliano Marcellino & Gian Luigi Mazzi & Tommaso Proietti, 2010. "Survey data as coincident or leading indicators," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 109-131.
    6. Huang, Yu-Lieh, 2012. "Measuring business cycles: A temporal disaggregation model with regime switching," Economic Modelling, Elsevier, vol. 29(2), pages 283-290.
    7. Moauro, Filippo, 2010. "A monthly indicator of employment in the euro area: real time analysis of indirect estimates," MPRA Paper 27797, University Library of Munich, Germany, revised 30 Dec 2010.
    8. Proietti, Tommaso, 2008. "Estimation of Common Factors under Cross-Sectional and Temporal Aggregation Constraints: Nowcasting Monthly GDP and its Main Components," MPRA Paper 6860, University Library of Munich, Germany.
    9. Vosen, Simeon & Schmidt, Torsten, 2012. "A monthly consumption indicator for Germany based on Internet search query data," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 19(7), pages 683-687.
    10. Cuevas Ángel & Quilis Enrique M. & Espasa Antoni, 2015. "Quarterly Regional GDP Flash Estimates by Means of Benchmarking and Chain Linking," Journal of Official Statistics, Sciendo, vol. 31(4), pages 627-647, December.
    11. Tommaso Proietti, 2011. "Multivariate temporal disaggregation with cross-sectional constraints," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(7), pages 1455-1466, June.
    12. Jürgen Bierbaumer-Polly & Sandra Bilek-Steindl, 2017. "Quarterly National Accounts – Manual for Austria. Description of Applied Methods and Data Sources," WIFO Studies, WIFO, number 60427, April.
    13. Raffaella Basile & Bruno Chiarini & Elisabetta Marzano, 2011. "Can we Rely upon Fiscal Policy Estimates in Countries with Unreported Production of 15 Per Cent (or more) of GDP?," CESifo Working Paper Series 3521, CESifo.
    14. Cuevas Rumín, Ángel & Quilis, Enrique M. & Espasa, Antoni, 2011. "Combining benchmarking and chain-linking for short-term regional forecasting," DES - Working Papers. Statistics and Econometrics. WS ws114130, Universidad Carlos III de Madrid. Departamento de Estadística.
    15. Marcus Scheiblecker & Sandra Steindl & Michael Wüger, 2007. "Quarterly National Accounts Inventory of Austria. Description of Applied Methods and Data Sources," WIFO Studies, WIFO, number 37249, April.
    16. Tommaso Proietti & Alessandro Giovannelli, 2021. "Nowcasting monthly GDP with big data: A model averaging approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(2), pages 683-706, April.
    17. Klaus Abberger & Michael Graff & Oliver Müller & Boriss Siliverstovs, 2022. "Imputing Monthly Values for Quarterly Time Series. An Application Performed with Swiss Business Cycle Data," CESifo Working Paper Series 10191, CESifo.
    18. Willie Lahari & Alfred A. Haug & Arlene Garces-Ozanne, 2011. "Estimating Quarterly Gdp Data For The South Pacific Island Nations," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 56(01), pages 97-112.
    19. Abdullah Tahir & Jameel Ahmed & Waqas Ahmed, 2018. "Robust Quarterization of GDP and Determination of Business Cycle Dates for IGC Partner Countries," SBP Working Paper Series 97, State Bank of Pakistan, Research Department.
    20. Guay, Alain & Maurin, Alain, 2015. "Disaggregation methods based on MIDAS regression," Economic Modelling, Elsevier, vol. 50(C), pages 123-129.

    More about this item

    Keywords

    Quarterly real GDP series; temporal disaggregation; business cycles; New Zealand;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • E01 - Macroeconomics and Monetary Economics - - General - - - Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

    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:mtu:wpaper:07_13. 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: Maxine Watene (email available below). General contact details of provider: https://edirc.repec.org/data/motuenz.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.