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Alternative methods for forecasting GDP

Author Info

Listed author(s):
  • Dominique Guegan

    ()

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, PSE - Paris School of Economics)

  • Patrick Rakotomarolahy

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique)

Abstract

An empirical forecast accuracy comparison of the non-parametric method, known as multivariate Nearest Neighbor method, with parametric VAR modelling is conducted on the euro area GDP. Using both methods for nowcasting and forecasting the GDP, through the estimation of economic indicators plugged in the bridge equations, we get more accurate forecasts when using nearest neighbor method. We prove also the asymptotic normality of the multivariate k-nearest neighbor regression estimator for dependent time series, providing confidence intervals for point forecast in time series.

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Paper provided by HAL in its series Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) with number halshs-00511979.

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Date of creation: Dec 2010
Publication status: Published in R. Barnett, F. Jawady. Nonlinear Modeling of Economic and Financial Time-Series, Emerald Publishers, Chapiter 5 (29 p.), 2010, Series International Symposia in Economic Theory and Econometrics - n°21
Handle: RePEc:hal:cesptp:halshs-00511979
Note: View the original document on HAL open archive server: https://halshs.archives-ouvertes.fr/halshs-00511979
Contact details of provider: Web page: https://hal.archives-ouvertes.fr/

Keywords: Forecast; Economic indicators; GDP; Euro area; VAR; Multivariate k nearest neighbor regression; Asymptotic normality;

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Find related papers by JEL classification:
  • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
  • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
  • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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  1. Evan F. Koenig & Sheila Dolmas & Jeremy Piger, 2003. "The Use and Abuse of Real-Time Data in Economic Forecasting," The Review of Economics and Statistics, MIT Press, vol. 85(3), pages 618-628, August.
  2. Dominique Guégan & Nicolas Huck, 2005. "On the use of Nearest Neighbors in finance," Finance, Presses universitaires de Grenoble, vol. 26(2), pages 67-86.
  3. Laurent Ferrara & Dominique Guégan & Patrick Rakotomarolahy, 2010. "GDP nowcasting with ragged-edge data: a semi-parametric modeling," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 186-199.
  4. Joseph P. Byrne & E. Philip Davis, 2005. "Investment and Uncertainty in the G7," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 141(1), pages 1-32, April.
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Cited by:

  1. Jana Juriová, 2015. "The role of foreign sentiment in small open economy," International Journal of Economic Sciences, International Institute of Social and Economic Sciences, vol. 4(2), pages 57-68, June.

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