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Forecasting business and consumer surveys indicators-a time-series models competition

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  • Miquel Clar
  • Juan-Carlos Duque
  • Rosina Moreno

Abstract

The objective of this article is to compare different time-series methods for the short-run forecasting of Business and Consumer Survey Indicators. We consider all available data taken from the Business and Consumer Survey Indicators for the Euro area between 1985 and 2002. The main results of the forecast competition are offered not only for raw data but we also consider the effects of seasonality and removing outliers on forecast accuracy. In most cases, the univariate autoregressions were not outperformed by the other methods. As for the effect of seasonal adjustment methods and the use of data from which outliers have been removed, we obtain that the use of raw data has little effect on forecast accuracy. The forecasting performance of qualitative indicators is important since enlarging the observed time series of these indicators with forecast intervals may help in interpreting and assessing the implications of the current situation and can be used as an input in quantitative forecast models.

Suggested Citation

  • Miquel Clar & Juan-Carlos Duque & Rosina Moreno, 2007. "Forecasting business and consumer surveys indicators-a time-series models competition," Applied Economics, Taylor & Francis Journals, vol. 39(20), pages 2565-2580.
  • Handle: RePEc:taf:applec:v:39:y:2007:i:20:p:2565-2580
    DOI: 10.1080/00036840600690272
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    Citations

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    Cited by:

    1. Gómez Cardona, Sebastián & Jaramillo, Alberto, 2009. "¿Es la coyuntura económica un resultado de las expectativas empresariales? Análisis de la EOIC 1990-2008," PERFIL DE COYUNTURA ECONÓMICA, UNIVERSIDAD DE ANTIOQUIA - CIE, December.
    2. Oscar Claveria & Salvador Torra, 2013. "“Forecasting Business surveys indicators: neural networks vs. time series models”," AQR Working Papers 201312, University of Barcelona, Regional Quantitative Analysis Group, revised Nov 2013.
    3. Oscar Claveria & Enric Monte & Salvador Torra, 2015. "“Self-organizing map analysis of agents' expectations. Different patterns of anticipation of the 2008 financial crisis”," IREA Working Papers 201511, University of Barcelona, Research Institute of Applied Economics, revised Mar 2015.
    4. Thomas Lux & Jaba Ghonghadze, 2011. "Modeling the Dynamics of EU Economic Sentiment Indicators: An Interaction-Based Approach," Post-Print hal-00711445, HAL.
    5. Antonis A Michis, 2011. "Denoised least squars forecasting of GDP changes using indexes of consumer and business sentiment," IFC Bulletins chapters,in: Bank for International Settlements (ed.), Proceedings of the IFC Conference on "Initiatives to address data gaps revealed by the financial crisis", Basel, 25-26 August 2010, volume 34, pages 383-392 Bank for International Settlements.
    6. Ghonghadze, Jaba & Lux, Thomas, 2009. "Modeling the dynamics of EU economic sentiment indicators: an interaction-based approach," Kiel Working Papers 1487, Kiel Institute for the World Economy (IfW).

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