The Multivariate k-Nearest Neighbor Model for Dependent Variables: One-Sided Estimation and Forecasting

Dominique Guegan () and Patrick Rakotomarolahy ()
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Dominique Guegan: Paris School of Economics - Centre d'Economie de la Sorbonne, https://centredeconomiesorbonne.cnrs.fr
Patrick Rakotomarolahy: Centre d'Economie de la Sorbonne, https://centredeconomiesorbonne.cnrs.fr

Documents de travail du Centre d'Economie de la Sorbonne from Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne

Abstract: Forecasting current quarter GDP is a permanent task inside the central banks. Many models are known and proposed to solve this problem. Thanks to new results on the asymptotic normality of the multivariate k-nearest neighbor regression estimate, we propose an interesting and new approach to solve in particular the forecasting of economic indicators, included GDP modelling. Considering dependent mixing data sets, we prove the asymptotic normality of multivariate k-nearest neighbor regression estimate under weak conditions, providing confidence intervals for point forecasts. We introduce an application for economic indicators of euro area, and compare our method with other classical ARMA-GARCH modelling

Keywords: Multivariate k-nearest neighbor; asymptotic normality of the regression; mixing time series; confidence intervals; forecasts; economic indicators; Euro area (search for similar items in EconPapers)
JEL-codes: C22 C53 E32 (search for similar items in EconPapers)
Pages: 25 pages
Date: 2009-07, Revised 2009-12
New Economics Papers: this item is included in nep-ecm and nep-for
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Citations: View citations in EconPapers (5)

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ftp://mse.univ-paris1.fr/pub/mse/CES2009/09050.pdf (application/pdf)

Related works:
Working Paper: The Multivariate k-Nearest Neighbor Model for Dependent Variables: One-Sided Estimation and Forecasting (2009)
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Persistent link: https://EconPapers.repec.org/RePEc:mse:cesdoc:09050

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