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Risk Minimization For Time Series Binary Choice With Variable Selection


  • Jiang, Wenxin
  • Tanner, Martin A.


This paper considers the problem of predicting binary choices by selecting from a possibly large set of candidate explanatory variables, which can include both exogenous variables and lagged dependent variables. We consider risk minimization with the risk function being the predictive classification error. We study the convergence rates of empirical risk minimization in both the frequentist and Bayesian approaches. The Bayesian treatment uses a Gibbs posterior constructed directly from the empirical risk instead of using the usual likelihood-based posterior. Therefore these approaches do not require a correctly specified probability model. We show that the proposed methods have near optimal performance relative to a class of linear classification rules with selected variables. Such results in classification are obtained in a framework of dependent data with strong mixing.

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  • Jiang, Wenxin & Tanner, Martin A., 2010. "Risk Minimization For Time Series Binary Choice With Variable Selection," Econometric Theory, Cambridge University Press, vol. 26(05), pages 1437-1452, October.
  • Handle: RePEc:cup:etheor:v:26:y:2010:i:05:p:1437-1452_99

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    Cited by:

    1. Abhik Ghosh & Ayanendranath Basu, 2016. "Robust Bayes estimation using the density power divergence," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 68(2), pages 413-437, April.
    2. Le-Yu Chen & Sokbae Lee, 2016. "Best Subset Binary Prediction," Papers 1610.02738,, revised Nov 2017.
    3. Yao, Lili & Jiang, Wenxin, 2012. "On extensions of Hoeffding’s inequality for panel data," Statistics & Probability Letters, Elsevier, vol. 82(3), pages 446-454.

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