Semiparametric averaging of nonlinear marginal logistic regression and forecasting for time series classification

Rong Peng, Zudi Lu

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Binary classification is an important issue in many applications but mostly studied for independent data in the literature. A binary time series classification is investigated by proposing a semiparametric procedure named “Model Averaging nonlinear MArginal LOgistic Regressions” (MAMaLoR) for binary time series data based on the time series information of predictor variables. The procedure involves approximating the logistic multivariate conditional regression function by combining low-dimensional non-parametric nonlinear marginal logistic regressions, in the sense of Kullback-Leibler distance. A time series conditional likelihood method is suggested for estimating the optimal averaging weights together with local maximum likelihood estimations of the nonparametric marginal time series logistic (auto)regressions. The asymptotic properties of the procedure are established under mild conditions on the time series observations that are of -mixing property. The procedure is less computationally demanding and can avoid the “curse of dimensionality” for, and be easily applied to, high dimensional lagged information based nonlinear time series classification forecasting. The performances of the procedure are further confirmed both by Monte-Carlo simulation and an empirical study for market moving direction forecasting of the financial FTSE 100 index data.
Original languageEnglish
JournalEconometrics and Statistics
Early online date23 Nov 2021
DOIs
Publication statusE-pub ahead of print - 23 Nov 2021

Keywords / Materials (for Non-textual outputs)

  • binary time series classification
  • forecasting
  • model average
  • MAMaLoR
  • logistic marginal regression
  • semi-parametric likelihood estimation

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