IDEAS home Printed from https://ideas.repec.org/p/ucm/doicae/0501.html
   My bibliography  Save this paper

Econometric modeling of business Telecommunications demand using Retina and Finite Mixtues

Author

Listed:
  • Massimiliano Marinucci

    (Universidad Complutense de Madrid, Dpto. de Economía Cuantica)

  • Teodosio Pérez-Amaral

    (Universidad Complutense de Madrid, Dpto. de Economía Cuantica)

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.

Suggested Citation

  • Massimiliano Marinucci & Teodosio Pérez-Amaral, 2005. "Econometric modeling of business Telecommunications demand using Retina and Finite Mixtues," Documentos de Trabajo del ICAE 0501, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    • Handle: RePEc:ucm:doicae:0501
    as

    Download full text from publisher

    File URL: https://eprints.ucm.es/id/eprint/7863/1/0501.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Teodosio Perez‐Amaral & Giampiero M. Gallo & Halbert White, 2003. "A Flexible Tool for Model Building: the Relevant Transformation of the Inputs Network Approach (RETINA)," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(s1), pages 821-838, December.
    2. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    3. Jeffrey Rohlfs, 1974. "A Theory of Interdependent Demand for a Communications Service," Bell Journal of Economics, The RAND Corporation, vol. 5(1), pages 16-37, Spring.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Camila Epprecht & Dominique Guegan & Álvaro Veiga & Joel Correa da Rosa, 2017. "Variable selection and forecasting via automated methods for linear models: LASSO/adaLASSO and Autometrics," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00917797, HAL.
    2. Jennifer Castle & David Hendry, 2010. "Automatic Selection for Non-linear Models," Economics Series Working Papers 473, University of Oxford, Department of Economics.
    3. Michal Grajek, 2003. "Estimating Network Effects and Compatibility in Mobile Telecommunications," CIG Working Papers SP II 2003-26, Wissenschaftszentrum Berlin (WZB), Research Unit: Competition and Innovation (CIG).
    4. Munoz, Teresa Garin & Amaral, Teodosio Perez, 1996. "Demand for international telephone traffic in Spain: An econometric study using provincial panel data," Information Economics and Policy, Elsevier, vol. 8(4), pages 289-315, December.
    5. Jennifer Castle & David Hendry, 2013. "Semi-automatic Non-linear Model selection," Economics Series Working Papers 654, University of Oxford, Department of Economics.
    6. López, Rafael & Valarezo, Ángel & Pérez-Amaral, Teodosio, 2021. "Online education adoption in Spain 2008-2019. Drivers and impediments," 23rd ITS Biennial Conference, Online Conference / Gothenburg 2021. Digital societies and industrial transformations: Policies, markets, and technologies in a post-Covid world 238038, International Telecommunications Society (ITS).
    7. Camila Epprecht & Dominique Guegan & Álvaro Veiga, 2013. "Comparing variable selection techniques for linear regression: LASSO and Autometrics," Documents de travail du Centre d'Economie de la Sorbonne 13080, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    8. Ericsson Neil R., 2016. "Testing for and estimating structural breaks and other nonlinearities in a dynamic monetary sector," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(4), pages 377-398, September.
    9. Dominik Schreyer, 2019. "Football spectator no-show behaviour in the German Bundesliga," Applied Economics, Taylor & Francis Journals, vol. 51(45), pages 4882-4901, September.
    10. S. Arunachalam & Sridhar N. Ramaswami & Pol Herrmann & Doug Walker, 2018. "Innovation pathway to profitability: the role of entrepreneurial orientation and marketing capabilities," Journal of the Academy of Marketing Science, Springer, vol. 46(4), pages 744-766, July.
    11. Timothy Erickson & Toni M. Whited, 2000. "Measurement Error and the Relationship between Investment and q," Journal of Political Economy, University of Chicago Press, vol. 108(5), pages 1027-1057, October.
    12. Paul W. Miller & Barry R. Chiswick, 2002. "Immigrant earnings: Language skills, linguistic concentrations and the business cycle," Journal of Population Economics, Springer;European Society for Population Economics, vol. 15(1), pages 31-57.
    13. Fors, Gunnar & Zejan, Mario, 1996. "Overseas R&D by Multinationals in foreign Centers of Excellence," SSE/EFI Working Paper Series in Economics and Finance 111, Stockholm School of Economics.
    14. Rodrigo M. S. Moita & Claudio Paiva, 2013. "Political Price Cycles in Regulated Industries: Theory and Evidence," American Economic Journal: Economic Policy, American Economic Association, vol. 5(1), pages 94-121, February.
    15. repec:spo:wpmain:info:hdl:2441/7172 is not listed on IDEAS
    16. Butler, Marty & Leone, Andrew J. & Willenborg, Michael, 2004. "An empirical analysis of auditor reporting and its association with abnormal accruals," Journal of Accounting and Economics, Elsevier, vol. 37(2), pages 139-165, June.
    17. Baiyegunhi, L.J.S. & Oppong, B.B., 2016. "Commercialisation of mopane worm (Imbrasia belina) in rural households in Limpopo Province, South Africa," Forest Policy and Economics, Elsevier, vol. 62(C), pages 141-148.
    18. MacKinnon, J G, 1989. "Heteroskedasticity-Robust Tests for Structural Change," Empirical Economics, Springer, vol. 14(2), pages 77-92.
    19. Fenech, Jean-Pierre & Skully, Michael & Xuguang, Han, 2014. "Franking credits and market reactions: Evidence from the Australian convertible security market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 32(C), pages 1-19.
    20. François Desmoulins-Lebeault & Jean-François Gajewski & Luc Meunier, 2018. "Personality and Risk Aversion," Economics Bulletin, AccessEcon, vol. 38(1), pages 472-489.
    21. Benjamin M. Blau & Ryan J. Whitby, 2014. "Speculative Trading In Reits," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 37(1), pages 55-74, February.

    More about this item

    Keywords

    Telecommunication Demand Models; Local calls; Inter-LATA calls; intra-LATA calls; Retina; Flexible Functional Forms; Heterogeneity; Finite Mixtures.;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ucm:doicae:0501. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Águeda González Abad (email available below). General contact details of provider: https://edirc.repec.org/data/feucmes.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.