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Autocorrelated unreplicated linear functional relationship model for multivariate time series data

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  • Yun Fah Chang
  • Sing Yan Looi
  • Wei Yeing Pan
  • Shin Zhu Sim

Abstract

The conventional practices in handling cross-sectional data treated the explanatory variables as fixed variables without measurement errors. This article proposed a novel autocorrelated unreplicated linear functional relationship model (AULFR) model to accommodate the autocorrelated errors in the measurement errors model. Some basic properties of the model have been derived. A modified backshift operator is used in transforming the autocorrelated error into uncorrelated error. Simulation studies show that AULFR outperforms other benchmarking models even with a relatively small training data percentage or a sample size of 100 training observations. The application of the AULFR model on an actual economic case shows consistent results with simulation studies. The advantages of the proposed model are (i) it models the relationship between a time-based dependent variable and a set of time-based explanatory variables which subjected to measurement errors, and (ii) it can predict the current or future values of both dependent and explanatory variables using historical data.

Suggested Citation

  • Yun Fah Chang & Sing Yan Looi & Wei Yeing Pan & Shin Zhu Sim, 2024. "Autocorrelated unreplicated linear functional relationship model for multivariate time series data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 53(20), pages 7244-7261, October.
    • Handle: RePEc:taf:lstaxx:v:53:y:2024:i:20:p:7244-7261
    DOI: 10.1080/03610926.2023.2263110
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