IDEAS home Printed from https://ideas.repec.org/a/taf/apeclt/v21y2014i2p135-139.html
   My bibliography  Save this article

Survey data and short-term forecasts of Swedish GDP growth

Author

Listed:
  • P�r Österholm

Abstract

In this article, we evaluate forecasting models for Swedish GDP growth which make use of data from Sweden's most important business survey, the Economic Tendency Survey . Employing nine years of quarterly real-time data, we conduct an out-of-sample forecast exercise. Results indicate that the survey data have informational value that can be used to improve forecasts, thereby confirming the empirical relevance of survey data for GDP forecasters.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:apeclt:v:21:y:2014:i:2:p:135-139
    DOI: 10.1080/13504851.2013.844317
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/13504851.2013.844317
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/13504851.2013.844317?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Armstrong, J. Scott, 2007. "Significance tests harm progress in forecasting," International Journal of Forecasting, Elsevier, vol. 23(2), pages 321-327.
    2. Johansson, Kerstin, 1998. "Exports in the Econometric Model KOSMOS," Working Papers 62, National Institute of Economic Research.
    3. Bańbura, Marta & Rünstler, Gerhard, 2011. "A look into the factor model black box: Publication lags and the role of hard and soft data in forecasting GDP," International Journal of Forecasting, Elsevier, vol. 27(2), pages 333-346.
    4. Barot, Bharat & Yang, Zan, 2002. "House Prices and Housing Investment in Sweden and the United Kingdom: Econometric Analysis for the Period 1970-1998," Working Papers 80, National Institute of Economic Research.
    5. Url, Thomas, 1996. "Internationalists, Regionalists, or Eurocentrists," Working Papers 51, National Institute of Economic Research.
    6. Öller, Lars-Erik & Barot, Bharat, 1999. "Comparing the Accuracy of European GDP Forecasts," Working Papers 64, National Institute of Economic Research.
    7. Christian Dreger & Christian Schumacher, 2005. "Out-of-sample Performance of Leading Indicators for the German Business Cycle: Single vs. Combined Forecasts," Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2005(1), pages 71-87.
    8. Lundvik, Petter, 1996. "Generational Accounting in a Small Open Economy," Working Papers 49, National Institute of Economic Research.
    9. 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.
    10. Sydney C. Ludvigson, 2004. "Consumer Confidence and Consumer Spending," Journal of Economic Perspectives, American Economic Association, vol. 18(2), pages 29-50, Spring.
    11. Beechey, Meredith & Österholm, Pär, 2010. "Forecasting inflation in an inflation-targeting regime: A role for informative steady-state priors," International Journal of Forecasting, Elsevier, vol. 26(2), pages 248-264, April.
    12. Croushore, Dean & Stark, Tom, 2001. "A real-time data set for macroeconomists," Journal of Econometrics, Elsevier, vol. 105(1), pages 111-130, November.
    13. 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.
    14. Huhtala, Anni & Samakovlis, Eva, 2003. "Green Accounting, Air Pollution and Health," Working Papers 82, National Institute of Economic Research.
    15. 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.
    16. Gren, Ing-Marie, 2003. "Monetary Green Accounting and Ecosystem Services," Working Papers 86, National Institute of Economic Research.
    17. 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.
    18. Eriksson, Kimmo & Karlander, Johan & Öller, Lars-Erik, 1996. "Hierarchical Assignments: Stability and Fairness," Working Papers 50, National Institute of Economic Research.
    19. Vartiainen, Juhana, 2010. "Interpreting Wage Bargaining Norms," Working Papers 116, National Institute of Economic Research.
    20. 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.
    21. Ashoka Mody & Mark P. Taylor, 2003. "The High-Yield Spread as a Predictor of Real Economic Activity: Evidence of a Financial Accelerator for the United States," IMF Staff Papers, Palgrave Macmillan, vol. 50(3), pages 1-3.
    22. Ruist, Erik, 1996. "Temporal Aggregation of an Econometric Equation," Working Papers 52, National Institute of Economic Research.
    23. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
    24. 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.
    25. Michelle T. Armesto & Kristie M. Engemann & Michael T. Owyang, 2010. "Forecasting with mixed frequencies," Review, Federal Reserve Bank of St. Louis, vol. 92(Nov), pages 521-536.
    26. James Mitchell & Richard J. Smith & Martin R. Weale, 2005. "Forecasting Manufacturing Output Growth Using Firm‐Level Survey Data," Manchester School, University of Manchester, vol. 73(4), pages 479-499, July.
    27. Boot, Arnoud W A & Greenbaum, Stuart I & Thakor, Anjan V, 1993. "Reputation and Discretion in Financial Contracting," American Economic Review, American Economic Association, vol. 83(5), pages 1165-1183, December.
    28. Dr Martin Weale & Dr. James Mitchell, 2005. "Forecasting manufacturing output growth using firm-level survey data," National Institute of Economic and Social Research (NIESR) Discussion Papers 251, National Institute of Economic and Social Research.
    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. Robert Lehmann, 2023. "The Forecasting Power of the ifo Business Survey," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 19(1), pages 43-94, March.
    2. 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.
    3. Мекенбаева Камила // Mekenbayeva Kamila & Karel Musil, 2017. "Система прогнозирования в Национальном Банке Казахстана: наукаст на основа опросов // Forecasting system at the National Bank of Kazakhstan: survey-based nowcasting," Working Papers #2017-1, National Bank of Kazakhstan.

    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. Österholm, Pär, 2013. "Forecasting Business Investment in the Short Term Using Survey Data," Working Papers 131, National Institute of Economic Research.
    2. Jan-Erik Antipin & Farid Jimmy Boumediene & Pär Österholm, 2014. "On the Usefulness of Constant Gain Least Squares when Forecasting the Unemployment Rate," Applied Economics Quarterly (formerly: Konjunkturpolitik), Duncker & Humblot GmbH, Berlin, vol. 60(4), pages 315-336.
    3. Meredith Beechey & Pär Österholm, 2014. "Central Bank Forecasts of Policy Interest Rates: An Evaluation of the First Years," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 43(1), pages 63-78, February.
    4. Östblom, Göran & Ljunggren Söderman, Maria & Sjöström, Magnus, 2010. "Analysing future solid waste generation - Soft linking a model of waste management with a CGE-model for Sweden," Working Papers 118, National Institute of Economic Research.
    5. Vartiainen, Juhana, 2010. "Interpreting Wage Bargaining Norms," Working Papers 116, National Institute of Economic Research.
    6. Gren, Ing-Marie, 2003. "Monetary Green Accounting and Ecosystem Services," Working Papers 86, National Institute of Economic Research.
    7. 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.
    8. Forslund, Johanna & Samakovlis, Eva & Vredin Johansson, Maria, 2006. "Matters Risk? The Allocation of Government Subsidies for Remediation of Contaminated Sites under the Local Investment Programme," Working Papers 94, National Institute of Economic Research.
    9. Marcus Mossfeldt & Par Osterholm, 2011. "The persistent labour-market effects of the financial crisis," Applied Economics Letters, Taylor & Francis Journals, vol. 18(7), pages 637-642.
    10. Lindström, Tomas, 2003. "The Role of High-Tech Capital Formation for Swedish Productivity Growth," Working Papers 83, National Institute of Economic Research.
    11. Boman, Mattias & Huhtala, Anni & Nilsson, Charlotte & Alroth, Sofia & Bostedt, Göran & Mattssson, Leif & Gong, Peichen, 2003. "Applying the Contingent Valuation Method in Resource Accounting: A Bold Proposal," Working Papers 85, National Institute of Economic Research.
    12. Lindén, Johan, 2004. "The Labor Market in KIMOD," Working Papers 89, National Institute of Economic Research.
    13. 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.
    14. Raoufina, Karine, 2016. "Forecasting Employment Growth in Sweden Using a Bayesian VAR Model," Working Papers 144, National Institute of Economic Research.
    15. 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.
    16. Golinelli, Roberto & Parigi, Giuseppe, 2014. "Tracking world trade and GDP in real time," International Journal of Forecasting, Elsevier, vol. 30(4), pages 847-862.
    17. Katja Heinisch & Rolf Scheufele, 2019. "Should Forecasters Use Real‐Time Data to Evaluate Leading Indicator Models for GDP Prediction? German Evidence," German Economic Review, Verein für Socialpolitik, vol. 20(4), pages 170-200, November.
    18. Rossi, Barbara, 2013. "Advances in Forecasting under Instability," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1203-1324, Elsevier.
    19. David de Antonio Liedo, 2014. "Nowcasting Belgium," Working Paper Research 256, National Bank of Belgium.
    20. Knut Are Aastveit & Francesco Ravazzolo & Herman K. van Dijk, 2018. "Combined Density Nowcasting in an Uncertain Economic Environment," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(1), pages 131-145, January.

    More about this item

    JEL classification:

    • E22 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Investment; Capital; Intangible Capital; Capacity
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications

    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:taf:apeclt:v:21:y:2014:i:2:p:135-139. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RAEL20 .

    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.