Risk minimization for time series binary choice with variable selection

Wenxin Jiang*, Martin A Tanner

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1437-1452
Number of pages16
JournalEconometric Theory
Volume26
Issue number5
DOIs
StatePublished - Oct 1 2010

ASJC Scopus subject areas

  • Economics and Econometrics
  • Social Sciences (miscellaneous)

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