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Do consumer-confidence indexes help forecast consumer spending in real time?

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  • Croushore, Dean

Abstract

Could a researcher or policy analyst use data reported from surveys of consumer confidence to improve forecasts of consumer spending? This issue has been examined in the literature previously, which reached the conclusion that consumer confidence helped improve the forecasts slightly. But that research was based on final, revised data and thus did not use the data that would have been available to forecasters in real time. This paper remedies that shortcoming, using the Real-Time Data Set for Macroeconomists to analyze the quality of forecasts made with indexes of consumer confidence. The main finding is that the indexes of consumer confidence are not of significant value in forecasting consumer spending. In fact, in some cases, they make the forecasts significantly worse.
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Suggested Citation

  • Croushore, Dean, 2005. "Do consumer-confidence indexes help forecast consumer spending in real time?," The North American Journal of Economics and Finance, Elsevier, vol. 16(3), pages 435-450, December.
  • Handle: RePEc:eee:ecofin:v:16:y:2005:i:3:p:435-450
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    References listed on IDEAS

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    1. Jason Bram & Sydney Ludvigson, 1998. "Does consumer confidence forecast household expenditure? a sentiment index horse race," Economic Policy Review, Federal Reserve Bank of New York, issue Jun, pages 59-78.
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    7. Stark, Tom & Croushore, Dean, 2002. "Reply to the comments on 'Forecasting with a real-time data set for macroeconomists'," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 563-567, December.
    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. Easaw, Joshy Z. & Heravi, Saeed M., 2004. "Evaluating consumer sentiments as predictors of UK household consumption behavior: Are they accurate and useful?," International Journal of Forecasting, Elsevier, vol. 20(4), pages 671-681.
    10. Hamerle, Alfred & Liebig, Thilo & Scheule, Harald, 2004. "Forecasting Credit Portfolio Risk," Discussion Paper Series 2: Banking and Financial Studies 2004,01, Deutsche Bundesbank.
    11. 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.
    12. E. Philip Howrey, 2001. "The Predictive Power of the Index of Consumer Sentiment," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 32(1), pages 175-216.
    13. Tom Stark, 2000. "Does current-quarter information improve quarterly forecasts for the U.S. economy?," Working Papers 00-2, Federal Reserve Bank of Philadelphia.
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    Cited by:

    1. Siliverstovs Boriss & Kholodilin Konstantin A., 2012. "Assessing the Real-Time Informational Content of Macroeconomic Data Releases for Now-/Forecasting GDP: Evidence for Switzerland," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 232(4), pages 429-444, August.
    2. Md. Kabir Ahmed Chowdhury & G.M. Abul Kalam Azad, 2010. "Consumer Confidence in Financial Markets," Working Papers id:3265, eSocialSciences.
    3. Orlando Gomes, 2010. "Consumer confidence, endogenous growth and endogenous cycles," Journal of Economic Studies, Emerald Group Publishing, vol. 37(4), pages 377-404, September.
    4. Dean Croushore, 2011. "Frontiers of Real-Time Data Analysis," Journal of Economic Literature, American Economic Association, vol. 49(1), pages 72-100, March.
    5. Hara, Naoko & Ichiue, Hibiki, 2011. "Real-time analysis on Japan's labor productivity," Journal of the Japanese and International Economies, Elsevier, vol. 25(2), pages 107-130, June.
    6. Kajal Lahiri & George Monokroussos & Yongchen Zhao, 2016. "Forecasting Consumption: the Role of Consumer Confidence in Real Time with many Predictors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1254-1275, November.
    7. Aleksejs Melihovs & Svetlana Rusakova, 2005. "Short-Term Forecasting of Economic Development in Latvia Using Business and Consumer Survey Data," Working Papers 2005/04, Latvijas Banka.
    8. Torsten Schmidt & Simeon Vosen, 2009. "Forecasting Private Consumption: Survey-based Indicators vs. Google Trends," Ruhr Economic Papers 0155, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
    9. Hutson, Mark & Joutz, Fred & Stekler, Herman, 2014. "Interpreting and evaluating CESIfo's World Economic Survey directional forecasts," Economic Modelling, Elsevier, vol. 38(C), pages 6-11.
    10. S. Heravi & J. Easaw & R. Golinelli, 2016. "Generalized State-Dependent Models: A Multivariate Approach," Working Papers wp1067, Dipartimento Scienze Economiche, Universita' di Bologna.
    11. repec:zbw:rwirep:0208 is not listed on IDEAS
    12. Simeon Vosen & Torsten Schmidt, 2011. "Forecasting private consumption: survey‐based indicators vs. Google trends," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(6), pages 565-578, September.
    13. Robert B. Barsky & Eric R. Sims, 2012. "Information, Animal Spirits, and the Meaning of Innovations in Consumer Confidence," American Economic Review, American Economic Association, vol. 102(4), pages 1343-1377, June.
    14. Paradiso, Antonio & Kumar, Saten & Margani, Patrizia, 2014. "Are Italian consumer confidence adjustments asymmetric? A macroeconomic and psychological motives approach," Journal of Economic Psychology, Elsevier, vol. 43(C), pages 48-63.
    15. Boriss Siliverstovs, 2010. "Assessing Predictive Content of the KOF Barometer in Real Time," KOF Working papers 10-249, KOF Swiss Economic Institute, ETH Zurich.
    16. Vosen, Simeon & Schmidt, Torsten, 2012. "A monthly consumption indicator for Germany based on Internet search query data," EconStor Open Access Articles, ZBW - German National Library of Economics, pages 683-687.
    17. Baghestani, Hamid & AbuAl-Foul, Bassam M., 2017. "Comparing Federal Reserve, Blue Chip, and time series forecasts of US output growth," Journal of Economics and Business, Elsevier, vol. 89(C), pages 47-56.
    18. Dudek, Sławomir, 2008. "Consumer Survey Data and short-term forecasting of households consumption expenditures in Poland," MPRA Paper 19818, University Library of Munich, Germany.
    19. Boriss Siliverstovs, 2013. "Do business tendency surveys help in forecasting employment?: A real-time evidence for Switzerland," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2013(2), pages 129-151.
    20. Stephen Bruestle & W. Mark Crain, 2015. "A mean-variance approach to forecasting with the consumer confidence index," Applied Economics, Taylor & Francis Journals, vol. 47(23), pages 2430-2444, May.
    21. Konstantin Kholodilin & Boriss Siliverstovs, 2010. "Assessing the Real-Time Informational Content of Macroeconomic Data Releases for Now-/Forecasting GDP," KOF Working papers 10-251, KOF Swiss Economic Institute, ETH Zurich.
    22. repec:zbw:rwirep:0155 is not listed on IDEAS
    23. Boriss Siliverstovs, 2011. "The Real-Time Predictive Content of the KOF Economic Barometer," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 147(III), pages 353-375, September.
    24. Dean Baker & David Rosnick, 2014. "Stimulus and Fiscal Consolidation: The Evidence and Implications," IMK Working Paper 135-2014, IMK at the Hans Boeckler Foundation, Macroeconomic Policy Institute.
    25. 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.

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    JEL classification:

    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications

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