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Quasi-Real-Time Data of the Economic Tendency Survey

Author

Listed:
  • Maria Billstam

    (National Institute of Economic Research)

  • Kristina Frändén

    (Statistics Sweden)

  • Johan Samuelsson

    (National Institute of Economic Research)

  • Pär Österholm

    (Örebro University)

Abstract

Survey data from businesses and households are widely used for forecasting and economic analysis. In Sweden, the most important survey of this kind is the Economic Tendency Survey of the National Institute of Economic Research. A shortcoming with this survey is that real-time data of it largely are unavailable. In this paper, we describe how two quasi-real-time data sets of this survey have been constructed—one monthly and one quarterly. The term “quasi-real-time data” refers to data which are not actual real-time data but have been created in order to provide a close approximation to real-time data. The data sets consist of monthly/quarterly vintages of the most important series of the survey, including the main confidence indicators. A natural usage of these data sets is evaluations of model-based forecasts and nowcasts. We illustrate this with an application to Swedish GDP growth. This shows that several of the studied indicators from the Economic Tendency Survey appear to have positive nowcast content for GDP growth.

Suggested Citation

  • Maria Billstam & Kristina Frändén & Johan Samuelsson & Pär Österholm, 2017. "Quasi-Real-Time Data of the Economic Tendency Survey," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 13(1), pages 105-138, May.
    • Handle: RePEc:spr:jbuscr:v:13:y:2017:i:1:d:10.1007_s41549-017-0016-7
    DOI: 10.1007/s41549-017-0016-7
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    1. Armstrong, J. Scott, 2007. "Significance tests harm progress in forecasting," International Journal of Forecasting, Elsevier, vol. 23(2), pages 321-327.
    2. Bengt Assarsson & Pär Österholm, 2015. "Do Swedish Consumer Confidence Indicators Do What They Are Intended to Do?," Applied Economics Quarterly (formerly: Konjunkturpolitik), Duncker & Humblot GmbH, Berlin, vol. 61(4), pages 391-404.
    3. Herrmann, Heinz & Orphanides, Athanasios & Siklos, Pierre L., 2005. "Real-time data and monetary policy," The North American Journal of Economics and Finance, Elsevier, vol. 16(3), pages 271-276, December.
    4. Athanasios Orphanides, 2001. "Monetary Policy Rules Based on Real-Time Data," American Economic Review, American Economic Association, vol. 91(4), pages 964-985, September.
    5. Pesaran, M. Hashem & Schuermann, Til & Smith, L. Vanessa, 2009. "Forecasting economic and financial variables with global VARs," International Journal of Forecasting, Elsevier, vol. 25(4), pages 642-675, October.
    6. P�r Österholm, 2014. "Survey data and short-term forecasts of Swedish GDP growth," Applied Economics Letters, Taylor & Francis Journals, vol. 21(2), pages 135-139, January.
    7. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    8. Croushore, Dean & Stark, Tom, 2001. "A real-time data set for macroeconomists," Journal of Econometrics, Elsevier, vol. 105(1), pages 111-130, November.
    9. Mitchell, James, 2009. "Where are we now? The UK Recession and Nowcasting GDP Growth Using Statistical Models," National Institute Economic Review, National Institute of Economic and Social Research, vol. 209, pages 60-69, July.
    10. Hansson, Jesper & Jansson, Per & Lof, Marten, 2005. "Business survey data: Do they help in forecasting GDP growth?," International Journal of Forecasting, Elsevier, vol. 21(2), pages 377-389.
    11. Andersson, Michael K., 1998. "Do Long-Memory Models Have Long Memory?," SSE/EFI Working Paper Series in Economics and Finance 227, Stockholm School of Economics, revised 16 Mar 2000.
    12. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    13. Francis X. Diebold, 2015. "Comparing Predictive Accuracy, Twenty Years Later: A Personal Perspective on the Use and Abuse of Diebold-Mariano Tests," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(1), pages 1-1, January.
    14. Stark, Tom & Croushore, Dean, 2002. "Forecasting with a real-time data set for macroeconomists," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 507-531, December.
    15. Pesaran, M. Hashem & Schuermann, Til & Smith, L. Vanessa, 2009. "Rejoinder to comments on forecasting economic and financial variables with global VARs," International Journal of Forecasting, Elsevier, vol. 25(4), pages 703-715, October.
    16. Andersson, Michael K., 2000. "Do long-memory models have long memory?," International Journal of Forecasting, Elsevier, vol. 16(1), pages 121-124.
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    Cited by:

    1. Kristian Jönsson, 2020. "Machine Learning and Nowcasts of Swedish GDP," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 16(2), pages 123-134, November.
    2. Sorić, Petar & Lolić, Ivana & Claveria, Oscar & Monte, Enric & Torra, Salvador, 2019. "Unemployment expectations: A socio-demographic analysis of the effect of news," Labour Economics, Elsevier, vol. 60(C), pages 64-74.
    3. Kristian Jönsson, 2024. "Neighbor Weighting and Distance Metrics in Nearest Neighbor Nowcasting of Swedish GDP," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 22(4), pages 1077-1089, December.

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    More about this item

    Keywords

    Data revisions; Nowcasting;

    JEL classification:

    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

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