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Forecasting Private Consumption by Consumer Surveys

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  • Christian Dreger
  • Konstantin A. Kholodilin

Abstract

Survey-based indicators such as the consumer confidence are widely seen as leading indicators for economic activity, especially for the future path of private consumption. Although they receive high attention in the media, their forecasting power appears to be very limited. Therefore, this paper takes a fresh look on the survey data, which serve as a basis for the consumer confidence indicator (CCI) reported by the EU Commission for the euro area and individual member states. Different pooling methods are considered to exploit the information embedded in the consumer survey. Quantitative forecasts are based on Mixed Data Sampling (MIDAS) and bridge equations. While the CCI does not outperform an autoregressive benchmark for the majority of countries, the new indicators increase the forecasting performance. The gains over the CCI are striking for Italy and the entire euro area (20 percent). For Germany and France the gains seem to be lower, but are nevertheless substantial (10 to 15 percent). The best performing indicator should be built upon pre-selection methods, while data-driven aggregation methods should be preferred to determine the weights of the individual ingredients.

Suggested Citation

  • Christian Dreger & Konstantin A. Kholodilin, 2010. "Forecasting Private Consumption by Consumer Surveys," Discussion Papers of DIW Berlin 1066, DIW Berlin, German Institute for Economic Research.
  • Handle: RePEc:diw:diwwpp:dp1066
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    References listed on IDEAS

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    6. 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.
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    Cited by:

    1. Kladivko, Kamil & Österholm, Pär, 2020. "Can Households Predict where the Macroeconomy is Headed?," Working Papers 2020:11, Örebro University, School of Business.
    2. de Bondt, Gabe & Gieseck, Arne & Herrero, Pablo & Zekaite, Zivile, 2019. "Disaggregate income and wealth effects in the largest euro area countries," Research Technical Papers 15/RT/19, Central Bank of Ireland.
    3. 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.
    4. 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.
    5. Cláudia Duarte, 2016. "A Mixed Frequency Approach to Forecast Private Consumption with ATM/POS Data," Working Papers w201601, Banco de Portugal, Economics and Research Department.
    6. Gustavo Adolfo HERNANDEZ DIAZ & Margarita MARÍN JARAMILLO, 2016. "Pronóstico del Consumo Privado: Usando datos de alta frecuencia para el pronóstico de variables de baja frecuencia," Archivos de Economía 014828, Departamento Nacional de Planeación.
    7. Antonello D Agostino & Caterina Mendicino & Caterina Mendicino, 2015. "Can consumer confidence provide independent information on consumption spending?," Working Papers 2, European Stability Mechanism.
    8. Vincenzo Merella & Stephen E. Satchell, 2019. "Asset pricing with utility from external anticipation," Carlo Alberto Notebooks 589, Collegio Carlo Alberto.
    9. Willem Vanlaer & Samantha Bielen & Wim Marneffe, 2020. "Consumer Confidence and Household Saving Behaviors: A Cross-Country Empirical Analysis," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 147(2), pages 677-721, January.
    10. 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.
    11. Oscar Claveria & Enric Monte & Salvador Torra, 2019. "Empirical modelling of survey-based expectations for the design of economic indicators in five European regions," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 46(2), pages 205-227, May.
    12. Lindner, Axel & Heinisch, Katja, 2019. "Economic Sentiment in Europe: Disentangling Private Information from Public Knowledge," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203501, Verein für Socialpolitik / German Economic Association.
    13. Paradiso, Antonio & Rao, B. Bhaskara & Margani, Patrizia, 2011. "Time Series Estimates of the Italian Consumer Confidence Indicator," MPRA Paper 28395, University Library of Munich, Germany.
    14. Duarte, Cláudia & Rodrigues, Paulo M.M. & Rua, António, 2017. "A mixed frequency approach to the forecasting of private consumption with ATM/POS data," International Journal of Forecasting, Elsevier, vol. 33(1), pages 61-75.

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    More about this item

    Keywords

    Consumer confidence; consumption; nowcasting; mixed frequency data;
    All these keywords.

    JEL classification:

    • E21 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Consumption; Saving; Wealth
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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