Communications for Statistical Applications and Methods

  • 제29권1호
  • /
  • Pages.41-51
  • /
  • 2022
  • /
  • 2287-7843(pISSN)
  • /
  • 2383-4757(eISSN)
한국통계학회 (The Korean Statistical Society)
권호 표지
DOI QR코드

DOI QR Code

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)
  • 투고 : 2021.06.11
  • 심사 : 2021.10.28
  • 발행 : 2022.01.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

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.

키워드

과제정보

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).

참고문헌

  1. Baumeister C and Kilian L (2015). Forecasting the real price of oil in a changing world: a forecast combination approach, Journal of Business and Economic Statistics, 33, 338-351. https://doi.org/10.1080/07350015.2014.949342
  2. Bulut L (2018). Google Trends and the forecasting performance of exchange rate models, Journal of Forecasting, 37, 303-315. https://doi.org/10.1002/for.2500
  3. Carriere-Swallow Y and Labbe F (2013). Nowcasting with google trends in an emerging market, Journal of Forecasting, 32, 289-298. https://doi.org/10.1002/for.1252
  4. Cepni O, Guney IE, and Swanson NR (2019). Nowcasting and forecasting GDP in emerging markets using global financial and macroeconomic diffusion indexes, International Journal of Forecasting, 35, 555-572. https://doi.org/10.1016/j.ijforecast.2018.10.008
  5. Cho SJ and Shin DW (2016). An integrated heteroscedastic autoregressive model for forecasting realized volatilities, Journal of the Korean Statistical Society, 45, 371-380. https://doi.org/10.1016/j.jkss.2015.12.004
  6. Choi JE and Shin DW (2018). Forecasts for leverage heterogeneous autoregressive models with jumps and other covariates, Journal of Forecasting, 37, 691-704. https://doi.org/10.1002/for.2530
  7. Fantazzini D and Fomichev N (2014). Forecasting the real price of oil using online search data, International Journal of Computational Economics and Econometrics, 4, 4-31. https://doi.org/10.1504/IJCEE.2014.060284
  8. Guo JF and Ji Q (2013). How does market concern derived from the Internet affect oil prices?, Applied Energy, 112, 1536-1543. https://doi.org/10.1016/j.apenergy.2013.03.027
  9. Hansen PR, Lunde A, and Nason JM (2011). The model confidence set, Econometrica, 79, 453-497. https://doi.org/10.3982/ecta5771
  10. Kim HS and Shin DW (2019). Forecast of realized covariance matrix based on asymptotic distribution of the LU decomposition with an application for balancing minimum variance portfolio, Applied Economics Letters, 26, 661-668. https://doi.org/10.1080/13504851.2018.1489108
  11. Li J and Chen W (2014). Forecasting macroeconomic time series: LASSO-based approaches and their forecast combinations with dynamic factor models, International Journal of Forecasting, 30, 996-1015. https://doi.org/10.1016/j.ijforecast.2014.03.016
  12. Li X, Ma J, Wang S, and Zhang X (2015). How does Google search affect trader positions and crude oil prices?, Economic Modelling, 49, 162-171. https://doi.org/10.1016/j.econmod.2015.04.005
  13. Messner JW and Pinson P (2019). Online adaptive lasso estimation in vector autoregressive models for high dimensional wind power forecasting, International Journal of Forecasting, 35, 1485-1498. https://doi.org/10.1016/j.ijforecast.2018.02.001
  14. Nicholson WB, Matteson DS, and Bien J (2017). VARX-L: Structured regularization for large vector autoregressions with exogenous variables, International Journal of Forecasting, 33, 627-651. https://doi.org/10.1016/j.ijforecast.2017.01.003
  15. Niesert RF, Oorschot JA, Veldhuisen CP, Brons K, and Lange RJ (2020). Can Google search data help predict macroeconomic series?, International Journal of Forecasting, 36, 1163-1172. https://doi.org/10.1016/j.ijforecast.2018.12.006
  16. Sagaert YR, Aghezzaf EH, Kourentzes N, and Desmet B (2018). Tactical sales forecasting using a very large set of macroeconomic indicators, European Journal of Operational Research, 264, 558-569. https://doi.org/10.1016/j.ejor.2017.06.054
  17. Smeekes S and Wijler E (2018). Macroeconomic forecasting using penalized regression methods, International Journal of Forecasting, 34, 408-430. https://doi.org/10.1016/j.ijforecast.2018.01.001
  18. Stock JH and Watson MW (2005). An Empirical Comparison of Methods for Forecasting Using Many Predictors, Manuscript, Princeton University.
  19. Tarassow A (2019). Forecasting U.S. money growth using economic uncertainty measures and regularisation techniques, International Journal of Forecasting, 35, 443-457. https://doi.org/10.1016/j.ijforecast.2018.09.012
  20. Wen F, Gong X, and Cai S (2016). Forecasting the volatility of crude oil futures using HAR-type models with structural breaks, Energy Economics, 59, 400-413. https://doi.org/10.1016/j.eneco.2016.07.014
  21. Yu L, Zhao Y, Tang L, and Yang Z (2019). Online big data-driven oil consumption forecasting with Google trends, International Journal of Forecasting, 35, 213-223. https://doi.org/10.1016/j.ijforecast.2017.11.005
  22. Zhang JL, Zhang YJ, and Zhang L (2015). A novel hybrid method for crude oil price forecasting, Energy Economics, 49, 649-659. https://doi.org/10.1016/j.eneco.2015.02.018
  23. Zhao Y, Li J, and Yu L (2017). A deep learning ensemble approach for crude oil price forecasting, Energy Economics, 66, 9-16. https://doi.org/10.1016/j.eneco.2017.05.023