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Constructing Historical Euro Area Data

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  • Heather Anderson
  • Mardi Dungey
  • Denise R. Osborn
  • Farshid Vahid

Abstract

Time series analysis for the Euro Area requires the availability of sufficiently long historical data series, but the appropriate construction methodology has received little attention. The benchmark dataset, developed by the European Central Bank for use in its Area Wide Model (AWM), is based on fixed-weight aggregation across countries with historically distinct monetary policies and financial markets of varying international importance. This paper proposes a new methodology, based on the historical distance from monetary integration between core and periphery countries, for producing back-dated monetary and financial series for the Euro Area. The impact of using the new methodology versus the AWM data is illustrated through a structural VAR analysis and estimates of an international DSGE model. An important advantage of the new methodology is that it can be applied to develop appropriate series as new member countries join the Euro Area.

Suggested Citation

  • Heather Anderson & Mardi Dungey & Denise R. Osborn & Farshid Vahid, 2007. "Constructing Historical Euro Area Data," CAMA Working Papers 2007-18, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  • Handle: RePEc:een:camaaa:2007-18
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    Cited by:

    1. Mardi Dungey, 2010. "Discussion of The Economic Consequences of Oil Shocks: Differences across Countries and Time," RBA Annual Conference Volume,in: Renée Fry & Callum Jones & Christopher Kent (ed.), Inflation in an Era of Relative Price Shocks Reserve Bank of Australia.
    2. Michael Ehrmann & Marcel Fratzscher & Roberto Rigobon, 2011. "Stocks, bonds, money markets and exchange rates: measuring international financial transmission," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(6), pages 948-974, September.
    3. Pierre Siklos & Martin Bohl, 2009. "Asset Prices as Indicators of Euro Area Monetary Policy: An Empirical Assessment of Their Role in a Taylor Rule," Open Economies Review, Springer, vol. 20(1), pages 39-59, February.
    4. Lütkepohl Helmut, 2011. "Forecasting Nonlinear Aggregates and Aggregates with Time-varying Weights," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(1), pages 107-133, February.
    5. Heather M Anderson, 2010. "Discussion of Key Elements of Global Inflation," RBA Annual Conference Volume,in: Renée Fry & Callum Jones & Christopher Kent (ed.), Inflation in an Era of Relative Price Shocks Reserve Bank of Australia.
    6. Jean-Sébastien Pentecôte & Marilyne Huchet-Bourdon, 2009. "Shock asymmetries and distance to the Euro Area," Post-Print hal-00730072, HAL.
    7. Helmut Lütkepohl, 2010. "Forecasting Aggregated Time Series Variables: A Survey," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2010(2), pages 1-26.
    8. repec:rim:rimwps:32-07 is not listed on IDEAS
    9. Paredes, Joan & Pedregal, Diego J. & Pérez, Javier J., 2009. "A quarterly fiscal database for the euro area based on intra-annual fiscal information," Working Paper Series 1132, European Central Bank.
    10. Paredes, Joan & Pedregal, Diego J. & Pérez, Javier J., 2014. "Fiscal policy analysis in the euro area: Expanding the toolkit," Journal of Policy Modeling, Elsevier, vol. 36(5), pages 800-823.

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    JEL classification:

    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies

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