Simultaneous variable selection and structural identification for time-varying coefficient models

Ngai Hang CHAN*, Linhao GAO, Wilfredo PALMA

*Corresponding author for this work

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

1 Citation (Scopus)

Abstract

Time-varying coefficient models are important tools in time series analysis due to their flexibility to fit non-stationary data. To improve the accuracy of these models, it is important to identify covariates with null, constant and time-varying effects and to estimate their coefficients. This article proposes a combination of the local linear smoothing method and the adaptive group lasso penalty approach to achieve covariate identification and coefficient estimation. The penalty term consists of two parts. The first term penalizes the norm of the coefficient function, which is used to select relevant variables. The second term penalizes the norm of the derivative function, which assesses the constancy of the coefficient functions. The asymptotic properties of the proposed methodology are established. Performance of the proposed method is demonstrated using simulated data along with an application to the analysis of the air quality and health data in Hong Kong.
Original languageEnglish
Pages (from-to)511-531
JournalJournal of Time Series Analysis
Volume43
Issue number4
Online published3 Sept 2021
DOIs
Publication statusPublished - Jul 2022
Externally publishedYes

Research Keywords

  • group lasso
  • information criteria
  • local linear estimator
  • locally stationary processes
  • Time-varying coefficient models
  • variable selection

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This output contributes to the following UN Sustainable Development Goals (SDGs)

  • SDG 3 - Good Health and Well-being

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