Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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How to improve oil consumption forecast using google trends from online big data?: the structured regularization methods for large vector autoregressive model

  • Choi, Ji-Eun (Department of Statistics, Pukyong National University) ;
  • Shin, Dong Wan (Department of Statistics, Ewha Womans University)
  • Received : 2021.06.11
  • Accepted : 2021.10.28
  • Published : 2022.01.31
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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.

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Acknowledgement

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).

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