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Forecasting investment: A fishing contest using survey data

  • Sara Serra
  • José R. Maria

This paper assesses the usefulness of business surveys as a source of information for investment developments in Portugal. This will be achieved by what will be named a “fishing contest”, where the “participants” are bridge models, models based on principal components (derived from standard and non-standard methods), and models built with the outcome of partial least squares regressions. All models, based on quarterly data, are estimated using a general-to-specific approach and are designed to produce 1 to 4 out-of-sample direct forecasts. The accuracy of these forecasts is then compared with the one of autoregressive processes. The empirical evidence indicates that, in general, there is always a participant in the fishing context that produces a lower out-of-sample Root Mean Squared Error (RMSE) than the one associated with the autoregressive benchmark. In most cases, the combination of autoregressive processes with each participant reduces the RMSE further. A striking outcome is the relative accuracy of bridge models.

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Paper provided by Banco de Portugal, Economics and Research Department in its series Working Papers with number w200818.

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Date of creation: 2008
Date of revision:
Handle: RePEc:ptu:wpaper:w200818
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  1. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-44, January.
  2. Francisco Dias & Maximiano Pinheiro & António Rua, 2010. "Forecasting using targeted diffusion indexes," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(3), pages 341-352.
  3. Stock, J.H. & Watson, M.W., 1989. "New Indexes Of Coincident And Leading Economic Indicators," Papers 178d, Harvard - J.F. Kennedy School of Government.
  4. Jason Bram & Sydney Ludvigson, 1997. "Does consumer confidence forecast household expenditure?: A sentiment index horse race," Research Paper 9708, Federal Reserve Bank of New York.
  5. 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.
  6. A. W. Coats, 1996. "Introduction," History of Political Economy, Duke University Press, vol. 28(5), pages 3-11, Supplemen.
  7. Teresa Santero & Niels Westerlund, 1996. "Confidence Indicators and Their Relationship to Changes in Economic Activity," OECD Economics Department Working Papers 170, OECD Publishing.
  8. Alain N. Kabundi, 2004. "Estimation of Economic Growth in France Using Business Survey Data," IMF Working Papers 04/69, International Monetary Fund.
  9. Bai, Jushan & Ng, Serena, 2008. "Forecasting economic time series using targeted predictors," Journal of Econometrics, Elsevier, vol. 146(2), pages 304-317, October.
  10. Claveria, Oscar & Pons, Ernest & Ramos, Raul, 2007. "Business and consumer expectations and macroeconomic forecasts," International Journal of Forecasting, Elsevier, vol. 23(1), pages 47-69.
  11. Rünstler, Gerhard & Sédillot, Franck, 2003. "Short-term estimates of euro area real GDP by means of monthly data," Working Paper Series 0276, European Central Bank.
  12. Tashman, Leonard J., 2000. "Out-of-sample tests of forecasting accuracy: an analysis and review," International Journal of Forecasting, Elsevier, vol. 16(4), pages 437-450.
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