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“Forecasting Business surveys indicators: neural networks vs. time series models”

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Author Info

  • Oscar Claveria

    ()
    (Faculty of Economics, University of Barcelona)

  • Salvador Torra

    ()
    (Faculty of Economics, University of Barcelona)

Abstract

The objective of this paper is to compare different forecasting methods for the short run forecasting of Business Survey Indicators. We compare the forecasting accuracy of Artificial Neural Networks (ANN) vs. three different time series models: autoregressions (AR), autoregressive integrated moving average (ARIMA) and self-exciting threshold autoregressions (SETAR). We consider all the indicators of the question related to a country’s general situation regarding overall economy, capital expenditures and private consumption (present judgement, compared to same time last year, expected situation by the end of the next six months) of the World Economic Survey (WES) carried out by the Ifo Institute for Economic Research in co-operation with the International Chamber of Commerce. The forecast competition is undertaken for fourteen countries of the European Union. The main results of the forecast competition are offered for raw data for the period ranging from 1989 to 2008, using the last eight quarters for comparing the forecasting accuracy of the different techniques. ANN and ARIMA models outperform SETAR and AR models. Enlarging the observed time series of Business Survey Indicators is of upmost importance in order of assessing the implications of the current situation and its use as input in quantitative forecast models.

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File URL: http://www.ub.edu/irea/working_papers/2013/201320.pdf
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Bibliographic Info

Paper provided by University of Barcelona, Regional Quantitative Analysis Group in its series AQR Working Papers with number 201312.

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Length: 28 pages
Date of creation: Nov 2013
Date of revision: Nov 2013
Handle: RePEc:aqr:wpaper:201312

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Keywords: Business surveys; Forecasting; Time series models; Nonlinear models; Neural networks.;

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  1. David Hendry & Michael P. Clements, 2001. "Economic Forecasting: Some Lessons from Recent Research," Economics Papers 2002-W11, Economics Group, Nuffield College, University of Oxford.
  2. 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.
  3. James H. Stock & Mark W. Watson, 2001. "Forecasting Output and Inflation: The Role of Asset Prices," NBER Working Papers 8180, National Bureau of Economic Research, Inc.
  4. Francis X. Diebold & Glenn D. Rudebusch, 1987. "Scoring the leading indicators," Special Studies Papers 206, Board of Governors of the Federal Reserve System (U.S.).
  5. Stangl, Anna, 2009. "Essays on the Measurement of Economic Expectations," Munich Dissertations in Economics 9823, University of Munich, Department of Economics.
  6. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
  7. Hill, Tim & Marquez, Leorey & O'Connor, Marcus & Remus, William, 1994. "Artificial neural network models for forecasting and decision making," International Journal of Forecasting, Elsevier, vol. 10(1), pages 5-15, June.
  8. Anders Bredahl Kock & Timo Teräsvirta, 2010. "Forecasting with nonlinear time series models," CREATES Research Papers 2010-01, School of Economics and Management, University of Aarhus.
  9. Swanson, Norman R. & White, Halbert, 1997. "Forecasting economic time series using flexible versus fixed specification and linear versus nonlinear econometric models," International Journal of Forecasting, Elsevier, vol. 13(4), pages 439-461, December.
  10. Biart, Michel & Praet, Peter, 1987. "The contribution of opinion surveys in forecasting aggregate demand in the four main EC countries," Journal of Economic Psychology, Elsevier, vol. 8(4), pages 409-428, December.
  11. Nakamura, Emi, 2005. "Inflation forecasting using a neural network," Economics Letters, Elsevier, vol. 86(3), pages 373-378, March.
  12. Clements, Michael P & Smith, Jeremy, 1996. "A Monte Carlo Study of the Forecasting Performance of Empirical Setar Models," The Warwick Economics Research Paper Series (TWERPS) 464, University of Warwick, Department of Economics.
  13. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
  14. Qi, Min, 2001. "Predicting US recessions with leading indicators via neural network models," International Journal of Forecasting, Elsevier, vol. 17(3), pages 383-401.
  15. O Claveria & E Pons & J Surinach, 2006. "Quantification of Expectations. Are They Useful for Forecasting Inflation?," Economic Issues Journal Articles, Economic Issues, vol. 11(2), pages 19-38, September.
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