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Measuring Inflation and Growth Using Spanning Trees

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  • Hill, Robert J

Abstract

It is shown how most methods of measuring inflation and growth have an underlying spanning tree. The spanning tree whose resulting inflation (growth) estimates are least sensitive to the choice of index number formula can be computed using Kruskal's minimum spanning tree algorithm. Applying this algorithm to American, British, and Australian data sets, chaining is shown to be the best possible way of linking annual data. For quarterly data, the optimal method of linking depends on the amount of seasonality in the data.

Suggested Citation

  • Hill, Robert J, 2001. "Measuring Inflation and Growth Using Spanning Trees," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 42(1), pages 167-185, February.
    • Handle: RePEc:ier:iecrev:v:42:y:2001:i:1:p:167-85
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    Citations

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    Cited by:

    1. Barnett, William A. & Erwin Diewert, W. & Zellner, Arnold, 2011. "Introduction to measurement with theory," Journal of Econometrics, Elsevier, vol. 161(1), pages 1-5, March.
    2. Isela Téllez-León. & Francisco Venegas-Martínez. & Abigail Rodríguez-Nava., 2011. "Inflation Volatility and Growth in a Stochastic Small Open Economy: A Mixed Jump-Diffusion Approach," Economía: teoría y práctica, Universidad Autónoma Metropolitana, México, vol. 35(2), pages 131-156, Julio-Dic.
    3. Diewert, W. Erwin & Fox, Kevin J., 2017. "Substitution Bias in Multilateral Methods for CPI Construction using Scanner Data," Microeconomics.ca working papers erwin_diewert-2017-3, Vancouver School of Economics, revised 23 Mar 2017.
    4. Cheong, Siew Ann & Fornia, Robert Paulo & Lee, Gladys Hui Ting & Kok, Jun Liang & Yim, Woei Shyr & Xu, Danny Yuan & Zhang, Yiting, 2011. "The Japanese economy in crises: A time series segmentation study," Economics Discussion Papers 2011-24, Kiel Institute for the World Economy (IfW Kiel).
    5. Chihiro Shimizu & Erwin Diewert, 2023. "Scanner Data, Product Churn and Quality Adjustment," Working Papers e185, Tokyo Center for Economic Research.
    6. Diewert, W. Erwin & Fox, Kevin J., 2016. "Kevin J. Fox Interview of W. Erwin Diewert," Microeconomics.ca working papers erwin_diewert-2016-6, Vancouver School of Economics, revised 02 Jun 2016.
    7. Erwin Diewert, 2010. "New Methodological Developments For The International Comparison Program," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 56(s1), pages 11-31, June.
    8. Ehemann, Christian, 2007. "Evaluating and adjusting for chain drift in national economic accounts," Journal of Economics and Business, Elsevier, vol. 59(3), pages 256-273.
    9. Diewert, Erwin, 2007. "Index Numbers," Economics working papers diewert-07-01-03-08-17-23, Vancouver School of Economics, revised 31 Jan 2007.
    10. W. Erwin Diewert & Kevin J. Fox, 2020. "Measuring Real Consumption and CPI Bias under Lockdown Conditions," NBER Working Papers 27144, National Bureau of Economic Research, Inc.
    11. Kevin J. Fox & Peter Levell & Martin O'Connell, 2023. "Inflation measurement with high frequency data," IFS Working Papers W23/29, Institute for Fiscal Studies.
    12. W. Erwin Diewert, 2022. "Scanner Data, Elementary Price Indexes and the Chain Drift Problem," Springer Books, in: Duangkamon Chotikapanich & Alicia N. Rambaldi & Nicholas Rohde (ed.), Advances in Economic Measurement, chapter 0, pages 445-606, Springer.
    13. Diewert, Erwin & Marandola, Tina, 2018. "Scanner Data, Elementary Price Indexes and the Chain Drift Problem," Microeconomics.ca working papers tina_marandola-2018-9, Vancouver School of Economics, revised 10 Oct 2018.
    14. W. Erwin Diewert & Kevin J. Fox, 2015. "Output Growth and Inflation across Space and Time," Discussion Papers 2015-04, School of Economics, The University of New South Wales.
    15. Diewert, Erwin, 2008. "New Methodology for Linking the Regions," Economics working papers erwin_diewert-2008-9, Vancouver School of Economics, revised 09 Sep 2008.
    16. Cheong, Siew Ann & Fornia, Robert Paulo & Lee, Gladys Hui Ting & Kok, Jun Liang & Yim, Woei Shyr & Xu, Danny Yuan & Zhang, Yiting, 2012. "The Japanese economy in crises: A time series segmentation study," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 6, pages 1-81.
    17. Zhang, Yiting & Lee, Gladys Hui Ting & Wong, Jian Cheng & Kok, Jun Liang & Prusty, Manamohan & Cheong, Siew Ann, 2011. "Will the US economy recover in 2010? A minimal spanning tree study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(11), pages 2020-2050.
    18. Yao, Hongxing & Memon, Bilal Ahmed, 2019. "Network topology of FTSE 100 Index companies: From the perspective of Brexit," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1248-1262.

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