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LOL selection in high dimension

Author

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  • Mougeot, M.
  • Picard, D.
  • Tribouley, K.

Abstract

A selection procedure with no optimization step called LOLA, for Learning Out of Leaders with Adaptation is proposed. LOLA is an auto-driven algorithm with two thresholding steps. The consistency of the LOL procedure (the non adaptive version of LOLA) is proved under sparsity conditions and simulations are conducted to illustrate the practical good performances of LOLA. The behavior of the algorithm is studied when instrumental variables are artificially added without a priori significant connection to the model. Finally, the problem of empirically verifying the conditional convergence hypothesis used in economics concerning the growth rate is studied. To avoid unnecessary discussion about the choice and the pertinence of instrumental variables, the model is embedded in a very high dimensional setting. Using the LOLA algorithm, a solution for modeling the link between the growth rate and the initial level of the gross domestic product is provided and the convergence hypothesis is empirically proved.

Suggested Citation

  • Mougeot, M. & Picard, D. & Tribouley, K., 2014. "LOL selection in high dimension," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 743-757.
    • Handle: RePEc:eee:csdana:v:71:y:2014:i:c:p:743-757
    DOI: 10.1016/j.csda.2012.04.012
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    References listed on IDEAS

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    1. S. Darolles & Y. Fan & J. P. Florens & E. Renault, 2011. "Nonparametric Instrumental Regression," Econometrica, Econometric Society, vol. 79(5), pages 1541-1565, September.
    2. Jean-Pierre Florens & James Heckman & Costas Meghir & Edward Vytlacil, 2002. "Instrumental variables, local instrumental variables and control functions," CeMMAP working papers CWP15/02, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Mathilde Mougeot & Dominique Picard & Karine Tribouley, 2012. "Learning out of leaders," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 74(3), pages 475-513, June.
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