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Dynamic variable selection in dynamic logistic regression: an application to Internet subscription

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  • Andrés Ramírez-Hassan

    (Universidad EAFIT)

Abstract

We extend the dynamic model averaging framework for dynamic logistic regression proposed by McCormick et al. (Biometrics 68(1):23–30, 2012) to incorporate variable selection. This method of accommodating uncertainty regarding predictors is particularly appealing in scenarios where relevant predictors change through time, and there are potentially many of them, as a consequence, the computational burden is high. Simulation experiments demonstrate that our greedy variable selection strategy works well in identifying the relevant regressors. We apply our algorithm to uncover the determinants of Internet subscription in Medellín (Colombia) among 18 potential factors, and thus 262,144 potential models. Our results suggest that subscription to pay TV, household members studying, years of education and number of household members are positively associated with Internet subscription.

Suggested Citation

  • Andrés Ramírez-Hassan, 2020. "Dynamic variable selection in dynamic logistic regression: an application to Internet subscription," Empirical Economics, Springer, vol. 59(2), pages 909-932, August.
    • Handle: RePEc:spr:empeco:v:59:y:2020:i:2:d:10.1007_s00181-019-01644-1
    DOI: 10.1007/s00181-019-01644-1
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    1. P. J. Brown & M. Vannucci & T. Fearn, 2002. "Bayes model averaging with selection of regressors," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 519-536, August.
    2. Michael Jetter & Andrés Ramírez Hassan, 2015. "Want Export Diversification? Educate The Kids First," Economic Inquiry, Western Economic Association International, vol. 53(4), pages 1765-1782, October.
    3. Penard, Thierry & Poussing, Nicolas & Mukoko, Blaise & Tamokwe Piaptie, Georges Bertrand, 2015. "Internet adoption and usage patterns in Africa: Evidence from Cameroon," Technology in Society, Elsevier, vol. 42(C), pages 71-80.
    4. Raftery A.E. & Zheng Y., 2003. "Discussion: Performance of Bayesian Model Averaging," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 931-938, January.
    5. Valen E. Johnson & David Rossell, 2012. "Bayesian Model Selection in High-Dimensional Settings," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 649-660, June.
    6. Theo S. Eicher & Christian Henn & Chris Papageorgiou, 2012. "Trade creation and diversion revisited: Accounting for model uncertainty and natural trading partner effects," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(2), pages 296-321, March.
    7. Paul Schreyer, 2000. "The Contribution of Information and Communication Technology to Output Growth: A Study of the G7 Countries," OECD Science, Technology and Industry Working Papers 2000/2, OECD Publishing.
    8. Carmen Fernandez & Eduardo Ley & Mark F. J. Steel, 2001. "Model uncertainty in cross-country growth regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(5), pages 563-576.
    9. Hansen, Bruce E., 2005. "Challenges For Econometric Model Selection," Econometric Theory, Cambridge University Press, vol. 21(1), pages 60-68, February.
    10. Geweke, John & Amisano, Gianni, 2011. "Optimal prediction pools," Journal of Econometrics, Elsevier, vol. 164(1), pages 130-141, September.
    11. Robertson, Alastair & Soopramanien, Didier & Fildes, Robert, 2007. "A segment-based analysis of Internet service adoption among UK households," Technology in Society, Elsevier, vol. 29(3), pages 339-350.
    12. Rajshree Agarwal & A. Edward Day, 1998. "The Impact of the Internet on Economic Education," The Journal of Economic Education, Taylor & Francis Journals, vol. 29(2), pages 99-110, June.
    13. Xavier Sala-I-Martin & Gernot Doppelhofer & Ronald I. Miller, 2004. "Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach," American Economic Review, American Economic Association, vol. 94(4), pages 813-835, September.
    14. Tyler H. McCormick & Adrian E. Raftery & David Madigan & Randall S. Burd, 2012. "Dynamic Logistic Regression and Dynamic Model Averaging for Binary Classification," Biometrics, The International Biometric Society, vol. 68(1), pages 23-30, March.
    15. Rice, Ronald E. & Katz, James E., 0. "Comparing internet and mobile phone usage: digital divides of usage, adoption, and dropouts," Telecommunications Policy, Elsevier, vol. 27(8-9), pages 597-623, September.
    16. Harris, R-G, 1996. "The Internet as a GPT : Factor Market Implications," Discussion Papers dp97-01, Department of Economics, Simon Fraser University.
    17. G. Elliott & C. Granger & A. Timmermann (ed.), 2006. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 1, number 1.
    18. Casella, George & Moreno, Elias, 2006. "Objective Bayesian Variable Selection," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 157-167, March.
    19. Goldfarb, Avi & Prince, Jeff, 2008. "Internet adoption and usage patterns are different: Implications for the digital divide," Information Economics and Policy, Elsevier, vol. 20(1), pages 2-15, March.
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    More about this item

    Keywords

    Bayes factor; Dynamic model averaging; Internet subscription; Logistic model; MCMC; Variable selection;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software

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