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

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  • Dominique Guegan

    ()
    (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Paris I - Panthéon-Sorbonne, EEP-PSE - Ecole d'Économie de Paris - Paris School of Economics - Ecole d'Économie de Paris)

  • Patrick Rakotomarolahy

    ()
    (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Paris I - Panthéon-Sorbonne)

Abstract

This article gives the asymptotic properties of multivariate k-nearest neighbor regression estimators for dependent variables belonging to Rd, d > 1. The results derived here permit to provide consistent forecasts, and confidence intervals for time series An illustration of the method is given through the estimation of economic indicators used to compute the GDP with the bridge equations. An empirical forecast accuracy comparison is provided by comparing this non-parametric method with a parametric one based on ARIMA modelling that we consider as a benchmark because it is still often used in Central Banks to nowcast and forecast the GDP.

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Bibliographic Info

Paper provided by HAL in its series Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) with number halshs-00423871.

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Date of creation: Dec 2009
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Handle: RePEc:hal:cesptp:halshs-00423871

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Related research

Keywords: Multivariate k-nearest neighbor; asymptotic normality of the regression; mixing time series; confidence intervals; forecasts; economic indicators; euro area.;

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Cited by:
  1. Dominique Guegan & Patrick Rakotomarolahy, 2010. "A Short Note on the Nowcasting and the Forecasting of Euro-area GDP Using Non-Parametric Techniques," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers), HAL halshs-00460472, HAL.
  2. repec:ebl:ecbull:v:30:y:2010:i:1:p:508-518 is not listed on IDEAS

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