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The forecasting accuracy of models of post-award network deployment: An application of maximum score tests

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  • Madden, Gary
  • Mayer, Walter
  • Wu, Chen
  • Tran, Thien

Abstract

Each mobile network operator’s spectrum is assigned by national governments. Licenses awarded by auctions are tied to post-award network deployment obligations. Using data on 18 countries for the period 2000–2007, this study is the first to empirically forecast aftermarket performance by analysing whether these conditions are met in a timely fashion. The forecasts are conditioned on macroeconomic and market conditions, and package attributes. The models are evaluated based on Mayer and Wu’s (in press) maximum score tests. Traditional probit models are not robust to error misspecifications. However, Manski’s (1975, 1985) maximum score estimator only imposes median independence, and allows arbitrary heteroskedasticity. One obstacle to empirical implementation is the fact that the asymptotic distribution of the estimator cannot be used for hypothesis testing. Mayer and Wu address the problem using a ‘discretisation’ procedure. The tests do not impose additional assumptions on the data generating process, require a shorter computational time than subsampling, and allow the models to be misspecified. The test statistics reflect differences in forecasting accuracy under the null and alternative hypotheses.

Suggested Citation

  • Madden, Gary & Mayer, Walter & Wu, Chen & Tran, Thien, 2015. "The forecasting accuracy of models of post-award network deployment: An application of maximum score tests," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1153-1158.
  • Handle: RePEc:eee:intfor:v:31:y:2015:i:4:p:1153-1158
    DOI: 10.1016/j.ijforecast.2013.01.002
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    References listed on IDEAS

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    1. Delgado, Miguel A. & Rodriguez-Poo, Juan M. & Wolf, Michael, 2001. "Subsampling inference in cube root asymptotics with an application to Manski's maximum score estimator," Economics Letters, Elsevier, vol. 73(2), pages 241-250, November.
    2. Paul Klemperer, 2002. "What Really Matters in Auction Design," Journal of Economic Perspectives, American Economic Association, vol. 16(1), pages 169-189, Winter.
    3. Thomas W. Hazlett & Roberto E. Muñoz, 2009. "A welfare analysis of spectrum allocation policies," RAND Journal of Economics, RAND Corporation, vol. 40(3), pages 424-454, September.
    4. Elliott, Graham & Lieli, Robert P., 2013. "Predicting binary outcomes," Journal of Econometrics, Elsevier, vol. 174(1), pages 15-26.
    5. Cramton Peter & Schwartz Jesse A, 2002. "Collusive Bidding in the FCC Spectrum Auctions," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 1(1), pages 1-20, December.
    6. Manski, Charles F., 1985. "Semiparametric analysis of discrete response : Asymptotic properties of the maximum score estimator," Journal of Econometrics, Elsevier, vol. 27(3), pages 313-333, March.
    7. Peter Cramton, 2001. "Lessons Learned from the UK 3G Spectrum Auction," Papers of Peter Cramton 01nao, University of Maryland, Department of Economics - Peter Cramton, revised 03 Jan 2002.
    8. Gary Madden & Hasnat Ahmad, 2013. "3G spectrum auction aftermarket network deployment," Applied Economics Letters, Taylor & Francis Journals, vol. 20(3), pages 300-303, February.
    9. P. M. Robinson, 1991. "Consistent Nonparametric Entropy-Based Testing," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(3), pages 437-453.
    10. Manski, Charles F. & Thompson, T. Scott, 1989. "Estimation of best predictors of binary response," Journal of Econometrics, Elsevier, vol. 40(1), pages 97-123, January.
    11. Javier Hidalgo, 1999. "Nonparametric tests for model selection with time series data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 8(2), pages 365-398, December.
    12. Whang, Yoon-Jae & Andrews, Donald W. K., 1993. "Tests of specification for parametric and semiparametric models," Journal of Econometrics, Elsevier, vol. 57(1-3), pages 277-318.
    13. Andrea Prat & Tommaso M. Valletti, 2001. "Spectrum Auctious Versus Beauty Contests: Costs and Benefits," Rivista di Politica Economica, SIPI Spa, vol. 91(4), pages 65-114, April-May.
    14. McMillan, John, 1995. "Why auction the spectrum?," Telecommunications Policy, Elsevier, vol. 19(3), pages 191-199, April.
    15. Horowitz, Joel L, 1992. "A Smoothed Maximum Score Estimator for the Binary Response Model," Econometrica, Econometric Society, vol. 60(3), pages 505-531, May.
    16. Manski, Charles F., 1975. "Maximum score estimation of the stochastic utility model of choice," Journal of Econometrics, Elsevier, vol. 3(3), pages 205-228, August.
    17. Yatchew, Adonis John, 1992. "Nonparametric Regression Tests Based on Least Squares," Econometric Theory, Cambridge University Press, vol. 8(4), pages 435-451, December.
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