Econometric Modeling of Business Telecommunications Demand using RETINA and Finite Mixtures

Massimiliano Marinucci () and Teodosio Pérez-Amaral
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Teodosio Pérez-Amaral: Universidad Complutense Madrid

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Abstract: In this paper we estimate the business telecommunications demands for local,intra-LATA and inter-LATA services, using US data from a Bill Harvesting (R) survey carried out during 1997. We model heterogeneity, which is present among firms due to a variety of different business telecommunication needs, by estimating normal heteroskedastic mixture regressions. The results show that a three-component mixture model fits the demand for local services well, while a two-component structure is used to model intra-LATA and inter-LATA demand. We characterize the groups in terms of their differences among the coefficients, and then use RETINA to perform automatic model selection over an expanded candidate regressor set which includes heterogeneity parameters as well as transformations of the original variables. Our models improve substantially the in-sample fit as well the out-of-sample predictive ability over alternative candidate models. RETINA suggests that the final demand specification should include telephone equipment variables as relevant regressors. On the other hand, the output of the firm, as well as its physical extension, have second order, yet significant effects on the demand for telecommunication services. Estimated elasticities are different for the three demands but always positive for access form (single-line or private network).

Keywords: Telecommunication Demand Models; Local calls; inter-LATA calls; intra-LATA calls; RETINA; Flexible Functional Forms; Heterogeneity; Finite Mixtures. (search for similar items in EconPapers)
JEL-codes: C21 C51 C52 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-net
Date: 2005-05-10, Revised 2005-05-16
Note: Type of Document - pdf; pages: 39
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Persistent link: http://EconPapers.repec.org/RePEc:wpa:wuwpem:0505003

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