How to improve oil consumption forecast using google trends from online big data? the structured regularization methods for large vector autoregressive model

Ji Eun Choi, Dong Wan Shin

Research output: Contribution to journalArticlepeer-review

Abstract

We forecast the US oil consumption level taking advantage of google trends. The google trends are the search volumes of the specific search terms that people search on google. We focus on whether proper selection of google trend terms leads to an improvement in forecast performance for oil consumption. As the forecast models, we consider the least absolute shrinkage and selection operator (LASSO) regression and the structured regularization method for large vector autoregressive (VAR-L) model of Nicholson et al. (2017), which select automatically the google trend terms and the lags of the predictors. An out-of-sample forecast comparison reveals that reducing the high dimensional google trend data set to a low-dimensional data set by the LASSO and the VAR-L models produces better forecast performance for oil consumption compared to the frequently-used forecast models such as the autoregressive model, the autoregressive distributed lag model and the vector error correction model

Original languageEnglish
Pages (from-to)721-731
Number of pages11
JournalCommunications for Statistical Applications and Methods
Volume29
Issue number1
DOIs
StatePublished - 2022

Bibliographical note

Funding Information:
The authors are grateful of the reviewers whose comments clarify several points. This study was supported by a grant from the National Research Foundation of Korea (2019R1A2C1004679) and by the Pukyong National University Re-search Fund in 2021 (C-D-2021-0982). 1Corresponding author: Department of Statistics, Ewha Womans University, 52, Ewhayeodae-gil, Seodaemun-gu, Seoul 03760, Korea. E-mail: shindw@ewha.ac.kr

Funding Information:
The authors are grateful of the reviewers whose comments clarify several points. This study was supported by a grant from the National Research Foundation of Korea (2019R1A2C1004679) and by the Pukyong National University Research Fund in 2021 (C-D-2021-0982)

Publisher Copyright:
© 2022 The Korean Statistical Society, and Korean International Statistical Society. All rights reserved

Keywords

  • Dimension reduction
  • Google trends
  • Oil consumption forecast
  • Online big data
  • The least absolute shrinkage and selection operator (lasso)

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