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Estimating dynamic response models when the data are subject to different temporal aggregation

Author: Vanhonacker, Wilfried R. INSEAD Area: MarketingIn: Marketing Letters, vol. 1, no. 2, June 1990 Language: EnglishDescription: p. 125-138.Type of document: INSEAD ArticleNote: Please ask the Library for this articleAbstract: In estimating response models using secondary data, it can happen that the observations on the variables are subject to different temporal aggregation. Estimating a dynamic model with this type of data is not straightforward, particularly when: estimates with good statistical properties are desired; and full use of all information in the data is needed. This paper provides an overview and discussion of the various approaches to the estimation problem when independent variables are observed less frequently than the dependent variable. The superiority of one-step estimation procedures which simultaneously estimate the parameters and the missing disaggregated data points is established
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In estimating response models using secondary data, it can happen that the observations on the variables are subject to different temporal aggregation. Estimating a dynamic model with this type of data is not straightforward, particularly when: estimates with good statistical properties are desired; and full use of all information in the data is needed. This paper provides an overview and discussion of the various approaches to the estimation problem when independent variables are observed less frequently than the dependent variable. The superiority of one-step estimation procedures which simultaneously estimate the parameters and the missing disaggregated data points is established

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