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5. Alternative methods for forecasting GDP

Author

Guegan, Dominique, Rakotomarolahy, Patrick, Centre d'économie de la Sorbonne (CES), Université Paris 1 Panthéon-Sorbonne (UP1)-Centre National de la Recherche Scientifique (CNRS), Paris School of Economics (PSE), École des Ponts ParisTech (ENPC)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université Paris 1 Panthéon-Sorbonne (UP1)-Centre National de la Recherche Scientifique (CNRS)-École des hautes études en sciences sociales (EHESS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and R. Barnett, F. Jawady

Subjects
[MATH.MATH-PR]Mathematics [math]/Probability [math.PR], [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Euro area, Asymptotic normality, Forecast, Economic indicators, VAR, [STAT.TH]Statistics [stat]/Statistics Theory [stat.TH], [SHS.ECO]Humanities and Social Sciences/Economics and Finance, Multivariate k nearest neighbor regression, Physics::Atmospheric and Oceanic Physics, GDP
Abstract

1st International Symposium in Computational Economics and Finance; 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.

Published
2010

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