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Alternative methods for forecasting GDP

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    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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    File URL: ftp://mse.univ-paris1.fr/pub/mse/CES2010/10065.pdf
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    Bibliographic Info

    Paper provided by Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne in its series Documents de travail du Centre d'Economie de la Sorbonne with number 10065.

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    Length: 29 pages
    Date of creation: Jul 2010
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    Handle: RePEc:mse:cesdoc:10065

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    Keywords: Forecast; economic indicators; GDP; Euro area; VAR; multivariate k-nearest neighbor regression; asymptotic normality.;

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    1. Laurent Ferrara & Dominique Guegan & Patrick Rakotomarolahy, 2010. "GDP nowcasting with ragged-edge data: a semi-parametric modeling," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00460461, HAL.
    2. Andrew P Blake, 1999. "An Artifical Neural Network System of Leading Indicators," NIESR Discussion Papers 144, National Institute of Economic and Social Research.
    3. Evan Koenig & Sheila Dolmas & Jeremy M. Piger, 2002. "The use and abuse of 'real-time' data in economic forecasting," Working Papers 2001-015, Federal Reserve Bank of St. Louis.
    4. Nicolas Huck & Dominique Guegan, 2005. "On the use of nearest neighbors in finance," Post-Print halshs-00180858, HAL.
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