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Recursive least-squares approach to data transferability: exposition and numerical results

Author: Vanhonacker, Wilfried R. ; Price, LydiaINSEAD Area: Marketing Series: Working Paper ; 92/38/MKT Publisher: Fontainebleau : INSEAD, 1992.Language: EnglishDescription: 23 p.Type of document: INSEAD Working Paper Online Access: Click here Abstract: Data transferability refers to the transfer of information from a set of independently estimable models to a new but structurally equivalent model for which data on some predictors are missing. Using a random coefficient regression framework, this paper discusses a recursive least squares approach to execute the transfer and estimate all parameters of the new model. Numerical results document the method's sensitivity to practically relevant dimensions and, as such, establish its general applicabillity.
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Data transferability refers to the transfer of information from a set of independently estimable models to a new but structurally equivalent model for which data on some predictors are missing. Using a random coefficient regression framework, this paper discusses a recursive least squares approach to execute the transfer and estimate all parameters of the new model. Numerical results document the method's sensitivity to practically relevant dimensions and, as such, establish its general applicabillity.

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