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Abstract
This research aims to assess the possibility of the daily and weekly Google Trends Index (GTI) to predict the quarterly GDP growth. The U-MIDAS approach is utilized because it allows using of daily and weekly basis data to forecast quarterly indicators without aggregating them onto a quarterly basis hence it does not eliminate useful information on the daily and weekly data. This research uses quarterly GDP for the transportation sector and the accommodation and restaurant sector which are considered potential industries for the future of Indonesia's economy. The result shows that the daily basis GTI can effectively predict the quarterly GDP growth better than the weekly basis GTI based on the RSE scores.
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References
- E. M. Bah, Structural Transformation Paths Across Countries, Emerg. Mark. Finance Trade., 47 (2011) 5–19.
- E. Zahrotul Awaliyyah, S.-E. Chen, and R. Anindita, Analysis of Structural Transformation of Labor from Agriculture to Non-Agriculture in Asia, Agric. Soc. Econ. J., 20(4) (2020) 335–341.
- L. Schlogl and A. Sumner, Rethinking international development series Disrupted Development and the Future of Inequality in the Age of Automation. [Online]. Available: http://www.palgrave.com/gp/series/14501
- UN-Habitat, Structural transformation in developing countries: Cross Regional Analysis, (2016).
- K. Sen, Structural transformation around the world: Patterns and drivers, Asian Dev. Rev., 36(2) (2019) 1–31.
- A. Haryana, Economic and Welfare Impacts of Indonesia’s Tourism Sector, Jurnal Perencanaan Pembangunan: The Indonesian Journal of Development Planning, 4(3) (2020) 300–311.
- E. Ghysels, A. Sinko, and R. Valkanov, MIDAS Regressions: Further Results and New Directions, Econom Rev., 26(1) (2007) 53–90.
- F. Barsoum and S. Stankiewicz, Forecasting GDP growth using mixed-frequency models with switching regimes, Int. J. Forecast, 31(1) (2015) 33–50.
- D. Bilgin, D. Jankovic, and A. Lam, MIDAS regression using inflation and unemployment to predict GDP, 2018. Accessed: Aug. 18, 2022. [Online]. Available: http://mathstat.carleton.ca/~smills/2017-18/STAT4601-5703/Research%20Projects/2018%20Submissions/JankovicLamBilgin/Time%20Series.pdf
- T. and S. S. Kingnetr Natthaphat and Tungtrakul, Forecasting GDP Growth in Thailand with Different Leading Indicators Using MIDAS Regression Models, in Robustness in Econometrics, S. and H. V.-N. Kreinovich Vladik and Sriboonchitta, Ed. Cham: Springer International Publishing, (2017) 511–521.
- D. T. Utari and D. Rosadi, Forecasting Indonesian GDP growth using mixed data sampling (MIDAS) regression, 2018.
- T. Nakazawa, Constructing GDP Nowcasting Models Using Alternative Data Constructing GDP Nowcasting Models Using Alternative Data, 2022.
- A. Ghysels, E. Santa-Clara, P. Valkanov, E. Ghysels, P. Santa-Clara, and R. Valkanov, UCLA Recent Work Title The MIDAS Touch: Mixed Data Sampling Regression Models Permalink https://escholarship.org/uc/item/9mf223rs Publication Date The MIDAS Touch: Mixed Data Sampling Regression Models, [Online]. Available: https://escholarship.org/uc/item/9mf223rs
- D. T. Utari and H. Ilma, Comparison of methods for mixed data sampling (MIDAS) regression models to forecast Indonesian GDP using agricultural exports, in AIP Conference Proceedings, 2021(1) (2018).
- Google, FAQ about Google Trends Data - trends help, https://support.google.com/trends/answer/4365533?hl=en&ref_topic=6248052 (accessed Aug. 15, 2022).
- A. Mavragani and G. Ochoa, Google trends in infodemiology and infoveillance: Methodology framework, JMIR Public Health and Surveillance, 5(2) (2019).
- H. Choi and H. A. L. Varian, Predicting the Present with Google Trends, Economic Record, 88 (2012) 2–9.
- F. Wijnhoven, Sentiment Analysis and Google Trends Data for Predicting Car Sales Review spam View project Artificial intelligence for business decisions View project, 2017. [Online]. Available: http://aisel.aisnet.org/icis2017/DataScience/Presentations/1.
- F. Ahmed and M. Muzammil, Financial Market Prediction using Google Trends, 2017. [Online]. Available: www.ijacsa.thesai.org
- N. Woloszko, OECD Economics Department working papers, Tracking activity in real time with Google Trends, 2020. [Online]. Available: https://www.oecd-ilibrary.org/economics/oecd-economics-department-working-papers_18151973
- Y. Feng, G. Li, X. Sun, and J. Li, Forecasting the number of inbound tourists with Google Trends,” Procedia Comput. Sci., 162 (2019) 628–633.
- I. Ayuningtyas, I. Wirawati, Nowcasting tingkat penghunian kamar hotel menggunakan google trends, Seminar Nasional Official Statistic 2020, Politeknik Statistik STIS.
- T. Havranek and A. Zeynalov, Forecasting tourist arrivals: Google Trends meets mixed-frequency data, Tour. Econ., 27(1) (2021) 129–148.
- P. Massicotte and D. Eddelbuettel, Perform and display google trends queries [R package gtrendsr version 1.4.6], 2022. Accessed: Aug. 15, 2022. [Online]. Available: https://cran.microsoft.com/snapshot/2020-06 20/web/packages/gtrendsR/index.html
- P. Trasborg, Google trends categories, 2007. https://github.com/pat310/google-trends-api/wiki/Google-Trends-Categories
- F. B, Google trends: How to acquire daily data for broad time frames, https://medium.com/@bewerunge.franz/google-trends-how-to-acquire-daily-data-for-broad-time-frames-b6c6dfe200e6 (accessed Aug. 18, 2022).
- D. A. Dickey and W. A. Fuller, Distribution of the Estimators for Autoregressive Time Series with a Unit Root,” J. Am. Stat. Assoc., 74(366a) (1979) 427–431.
- E. Paparoditis and D. N. Politis, The asymptotic size and power of the augmented Dickey–Fuller test for a unit root, Econom Rev., 37(9) (2018) 955–973.
- P. C. B. Philips and P. Perron, Testing for a unit root in time series regression, Biometrika, 75(2) (1988) 335–346.
- T. R. Derrick and J. M. Thomas, Time-Series Analysis: The cross-correlation function, in Innovative Analyses of Human Movement, N. Stergiou, Ed. Champaign, Illinois: Human Kinetics Publishers, 2004, 189–205.
- A. E. Usoro, Some basic properties of cross-correlation functions of n-dimensional vector time series, J. Stat. Economet. Meth., 4(1) (2015) 63–71.
- C. Foroni, M. Marcellino, and C. Schumacher, U-Midas: Midas Regressions with Unrestricted Lag Polynomials, 2012. Accessed: Aug. 18, 2022. [Online]. Available: https://ssrn.com/abstract=2013819
- E. Ghysels, V. Kvedaras, and V. Zemlys, Mixed Frequency Data Sampling Regression Models: The R Package midasr, J. Stat. Softw., 72(4) (2016) 1–35.
- Zach, How to interpret residual standard error, 2021. https://www.statology.org/how-to-interpret-residual-standard-error/ (accessed Aug. 18, 2022).
- K. Karagoz and S. Ergun, Forecasting Monthly Inflation: A MIDAS Regression Application for Turkey, 2020.
References
E. M. Bah, Structural Transformation Paths Across Countries, Emerg. Mark. Finance Trade., 47 (2011) 5–19.
E. Zahrotul Awaliyyah, S.-E. Chen, and R. Anindita, Analysis of Structural Transformation of Labor from Agriculture to Non-Agriculture in Asia, Agric. Soc. Econ. J., 20(4) (2020) 335–341.
L. Schlogl and A. Sumner, Rethinking international development series Disrupted Development and the Future of Inequality in the Age of Automation. [Online]. Available: http://www.palgrave.com/gp/series/14501
UN-Habitat, Structural transformation in developing countries: Cross Regional Analysis, (2016).
K. Sen, Structural transformation around the world: Patterns and drivers, Asian Dev. Rev., 36(2) (2019) 1–31.
A. Haryana, Economic and Welfare Impacts of Indonesia’s Tourism Sector, Jurnal Perencanaan Pembangunan: The Indonesian Journal of Development Planning, 4(3) (2020) 300–311.
E. Ghysels, A. Sinko, and R. Valkanov, MIDAS Regressions: Further Results and New Directions, Econom Rev., 26(1) (2007) 53–90.
F. Barsoum and S. Stankiewicz, Forecasting GDP growth using mixed-frequency models with switching regimes, Int. J. Forecast, 31(1) (2015) 33–50.
D. Bilgin, D. Jankovic, and A. Lam, MIDAS regression using inflation and unemployment to predict GDP, 2018. Accessed: Aug. 18, 2022. [Online]. Available: http://mathstat.carleton.ca/~smills/2017-18/STAT4601-5703/Research%20Projects/2018%20Submissions/JankovicLamBilgin/Time%20Series.pdf
T. and S. S. Kingnetr Natthaphat and Tungtrakul, Forecasting GDP Growth in Thailand with Different Leading Indicators Using MIDAS Regression Models, in Robustness in Econometrics, S. and H. V.-N. Kreinovich Vladik and Sriboonchitta, Ed. Cham: Springer International Publishing, (2017) 511–521.
D. T. Utari and D. Rosadi, Forecasting Indonesian GDP growth using mixed data sampling (MIDAS) regression, 2018.
T. Nakazawa, Constructing GDP Nowcasting Models Using Alternative Data Constructing GDP Nowcasting Models Using Alternative Data, 2022.
A. Ghysels, E. Santa-Clara, P. Valkanov, E. Ghysels, P. Santa-Clara, and R. Valkanov, UCLA Recent Work Title The MIDAS Touch: Mixed Data Sampling Regression Models Permalink https://escholarship.org/uc/item/9mf223rs Publication Date The MIDAS Touch: Mixed Data Sampling Regression Models, [Online]. Available: https://escholarship.org/uc/item/9mf223rs
D. T. Utari and H. Ilma, Comparison of methods for mixed data sampling (MIDAS) regression models to forecast Indonesian GDP using agricultural exports, in AIP Conference Proceedings, 2021(1) (2018).
Google, FAQ about Google Trends Data - trends help, https://support.google.com/trends/answer/4365533?hl=en&ref_topic=6248052 (accessed Aug. 15, 2022).
A. Mavragani and G. Ochoa, Google trends in infodemiology and infoveillance: Methodology framework, JMIR Public Health and Surveillance, 5(2) (2019).
H. Choi and H. A. L. Varian, Predicting the Present with Google Trends, Economic Record, 88 (2012) 2–9.
F. Wijnhoven, Sentiment Analysis and Google Trends Data for Predicting Car Sales Review spam View project Artificial intelligence for business decisions View project, 2017. [Online]. Available: http://aisel.aisnet.org/icis2017/DataScience/Presentations/1.
F. Ahmed and M. Muzammil, Financial Market Prediction using Google Trends, 2017. [Online]. Available: www.ijacsa.thesai.org
N. Woloszko, OECD Economics Department working papers, Tracking activity in real time with Google Trends, 2020. [Online]. Available: https://www.oecd-ilibrary.org/economics/oecd-economics-department-working-papers_18151973
Y. Feng, G. Li, X. Sun, and J. Li, Forecasting the number of inbound tourists with Google Trends,” Procedia Comput. Sci., 162 (2019) 628–633.
I. Ayuningtyas, I. Wirawati, Nowcasting tingkat penghunian kamar hotel menggunakan google trends, Seminar Nasional Official Statistic 2020, Politeknik Statistik STIS.
T. Havranek and A. Zeynalov, Forecasting tourist arrivals: Google Trends meets mixed-frequency data, Tour. Econ., 27(1) (2021) 129–148.
P. Massicotte and D. Eddelbuettel, Perform and display google trends queries [R package gtrendsr version 1.4.6], 2022. Accessed: Aug. 15, 2022. [Online]. Available: https://cran.microsoft.com/snapshot/2020-06 20/web/packages/gtrendsR/index.html
P. Trasborg, Google trends categories, 2007. https://github.com/pat310/google-trends-api/wiki/Google-Trends-Categories
F. B, Google trends: How to acquire daily data for broad time frames, https://medium.com/@bewerunge.franz/google-trends-how-to-acquire-daily-data-for-broad-time-frames-b6c6dfe200e6 (accessed Aug. 18, 2022).
D. A. Dickey and W. A. Fuller, Distribution of the Estimators for Autoregressive Time Series with a Unit Root,” J. Am. Stat. Assoc., 74(366a) (1979) 427–431.
E. Paparoditis and D. N. Politis, The asymptotic size and power of the augmented Dickey–Fuller test for a unit root, Econom Rev., 37(9) (2018) 955–973.
P. C. B. Philips and P. Perron, Testing for a unit root in time series regression, Biometrika, 75(2) (1988) 335–346.
T. R. Derrick and J. M. Thomas, Time-Series Analysis: The cross-correlation function, in Innovative Analyses of Human Movement, N. Stergiou, Ed. Champaign, Illinois: Human Kinetics Publishers, 2004, 189–205.
A. E. Usoro, Some basic properties of cross-correlation functions of n-dimensional vector time series, J. Stat. Economet. Meth., 4(1) (2015) 63–71.
C. Foroni, M. Marcellino, and C. Schumacher, U-Midas: Midas Regressions with Unrestricted Lag Polynomials, 2012. Accessed: Aug. 18, 2022. [Online]. Available: https://ssrn.com/abstract=2013819
E. Ghysels, V. Kvedaras, and V. Zemlys, Mixed Frequency Data Sampling Regression Models: The R Package midasr, J. Stat. Softw., 72(4) (2016) 1–35.
Zach, How to interpret residual standard error, 2021. https://www.statology.org/how-to-interpret-residual-standard-error/ (accessed Aug. 18, 2022).
K. Karagoz and S. Ergun, Forecasting Monthly Inflation: A MIDAS Regression Application for Turkey, 2020.