Bootstrapping pairs in Distance-Based Regression

Eva Boj, M. Mercedes Claramunt Bielsa and Jose Fortiana Gregori
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Jose Fortiana Gregori: Universitat de Barcelona

No 154, Working Papers in Economics from Universitat de Barcelona. Espai de Recerca en Economia

Abstract: Distance-based regression is a prediction method consisting of two steps: from distances between observations we obtain latent variables which, in turn, are the regressors in an ordinary least squares linear model. Distances are computed from actually observed predictors by means of a suitable dissimilarity function. Being in general nonlinearly related with the response their selection by the usual F tests is unavailable. In this paper we propose a solution to this predictor selection problem, by defining generalized test statistics and adapting a non-parametric bootstrap method to estimate their p-values. We include a numerical example with automobile insurance data.

JEL-codes: C12 C14 C15 C80 G22 (search for similar items in EconPapers)
Pages: 22 pages
Date: 2006
New Economics Papers: this item is included in nep-ecm
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