Pelatihan Online Metode Kuantitatif: Model Runtun Waktu Terapan

https://doi.org/10.35912/jpu.v1i1.895
Published: Mar 9, 2022

Abstract:

Purpose: the aim of this training is to improve the skill and ability of participants to apply quantitative method of time series model in their researchers by using zoom application.

Method: the descriptive analysis is used to analyze this activity based on the responses of the participants in this training.

Results: based on the results of the training on the quantitative method of applied time series model, it can be concluded that this activity is very important so that participants can understand data and be able to enter data correctly so as not to cause problems when estimating and processing other statistical data.

Contribution: in general, the participants felt that they were very good at implementing this activity and they hoped that there would be additional activities that were more focused on practice so that participants could apply various time series models in research and teaching at their respective institutions. ?t is recommended that participants need further training related to the application of the theories that have been presented in this training in order participants are able to publish their papers in international indexed journals.

Keywords:
1. quantitative method
2. online training
3. applied time series
Authors:
1 . Aliasuddin Aliasuddin
2 . Nanda Rahmi
3 . Mirza Tabrani
4 . Nashrillah Nashrillah
5 . Kamal Fachrurrozi
View PDF
How to Cite
Aliasuddin, A., Rahmi, N., Tabrani, M., Nashrillah, N., & Fachrurrozi, K. (2022). Pelatihan Online Metode Kuantitatif: Model Runtun Waktu Terapan. Jurnal Pemberdayaan Umat, 1(1), 51–59. https://doi.org/10.35912/jpu.v1i1.895
Download Citation
  1. Endnote/Zotero/Mendeley (RIS)
  2. BibTeX

Downloads

Download data is not yet available.
Issue & Section
Vol. 1 No. 1 (2022): Februari
Articles
References

    Abdulkarim, F. M., Akinlaso, M. I., Hamid, B. A., & Ali, H. S. (2020). The nexus between oil price and islamic stock markets in Africa: A wavelet and Multivariate-GARCH approach. Borsa Istanbul Review, 20(2), 108-120. doi:https://doi.org/10.1016/j.bir.2019.11.001

    Aliasuddin, & Ramadhana, R. (2019). Dynamic hydroelectricy consumption and economic growth in APEC countries and India. Regional Science Inquiry, XI(3), 111-117.

    Aliasuddin, Gunawan, E., & Sari, Y. P. (2019). An application of the GMM model on economic growth in Indonesia. Opcion, 90(2), 524-540.

    Aliasuddin, Syahnur, S., & Malia. (2020). Inflation and unemployment in Southeast Asian Countries: A Panel GMM application on Phillips curve. Regional Science Inquiry, upcoming.

    Asteriou, D., & Hall, S. G. (2016). Applied Econometrics (Third Edition ed.). London: Palgrave Macmillan.

    Bangsawan, S., MS, M., Ahadiat, A., Ribhan, Kesumah, F. S., & Febrian, A. (2021). Pengembangan Desa Wisata Melalui Pelatihan dan Pembinaan. Yumary: Jurnal Pengabdian kepada Masyarakat, 2(2), 79-90. doi:https://doi.org/10.35912/jpm.v2i2.615

    BI. (2020, May 18). Statistik Ekonomi Keuangan Indonesia. Retrieved from Bank Indonesia: www.bi.go.id

    Chekenya, N. S., & Dzingirai, C. (2020). The impact of the presence of autoregressive conditional heteroscedasticity (ARCH) effects on spurious regressions. Scientific African, 8, e00382. doi:https://doi.org/10.1016/j.sciaf.2020.e00382

    Ghysels, E., & Marcellino, M. (2018). Applied Economic Forecasting Using Time Series Methods. New York: Oxford University Press.

    Lutkepohl, H. (2007). New Introduction to Multiple Time Series Analysis. Berlin: Springer.

    Mikkael, R. H., Touana, H., & Takrim, M. (2020). PkM pelatihan peningkatan usaha mikro dalam mewujudkan smart businessmelalui smartphone di masa pandemi Covid-19. Yumary: Jurnal Pengabdian kepada Masyarakat, 1(1), 35-40. doi:https://doi.org/10.35912/jpm.v1i1.75

    Mishkin, F. S. (2019). The Economics of Money, Banking, and Financial Markets (Twelfth Edition, Global Edition ed.). London: Pearson Education Limited.

    Nguyen, T., Chaiechi, T., Eagle, L., & Low, D. (2020). Dynamic impacts of SME stock market development and innovation on macroeconomic indicators: A Post-Keynesian approach. Economic Analysis and Policy, 68, 327-347. doi:https://doi.org/10.1016/j.eap.2020.10.002

    Olayungbo, D. O. (2021). Global oil price and food prices in food importing and oil exporting developing countries: A panel ARDL analysis. Heliyon, 7(3), e06357. doi:https://doi.org/10.1016/j.heliyon.2021.e06357

    Qiyami, K. E., & Nilamsari, W. (2021). Pengembangan Kreativitas dan Produktivitas Siswa-Siswi Sekolah Dasar Islam Al-Azhar 8 Kembangan Jakarta Barat melalui Program Media Creative Class. Yumary: Jurnal Pengabdian kepada Masyarakat, 2(1), 39-49. doi:https://doi.org/10.35912/jpm.v2i1.390

    Roy, G., & Sharma, S. (2021). Measuring the role of factors on website effectiveness using vector autoregressive model. Journal of Retailing and Consumer Services, 62, 102656. doi:https://doi.org/10.1016/j.jretconser.2021.102656

    Siregar, M. I., Khamisah, N., Maryati, S., Pratiwi, T. S., L. D., H. F., . . . Kesuma, N. (2021). Sosialisasi dan Pelatihan Terkait Media Daring Google Classroom dan Google Form di Masa Pandemi Covid 19 pada Sekolah Dasar Negeri 23 Palembang. Yumary: Jurnal Pengabdian kepada Masyarakat, 2(2), 69-77. doi:https://doi.org/10.35912/jpm.v2i2.

    Stoian, A., & Iorgulescu, F. (2020). Fiscal policy and stock market efficiency: An ARDL Bounds Testing approach. Economic Modelling, 90, 406-416. doi:https://doi.org/10.1016/j.econmod.2019.12.023

    Sun, Y., Han, A., Hong, Y., & Wang, S. (2018). Threshold autoregressive models for interval-valued time series data. Journal of Econometrics, 206(2), 414-446. doi:https://doi.org/10.1016/j.jeconom.2018.06.009

  1. Abdulkarim, F. M., Akinlaso, M. I., Hamid, B. A., & Ali, H. S. (2020). The nexus between oil price and islamic stock markets in Africa: A wavelet and Multivariate-GARCH approach. Borsa Istanbul Review, 20(2), 108-120. doi:https://doi.org/10.1016/j.bir.2019.11.001
  2. Aliasuddin, & Ramadhana, R. (2019). Dynamic hydroelectricy consumption and economic growth in APEC countries and India. Regional Science Inquiry, XI(3), 111-117.
  3. Aliasuddin, Gunawan, E., & Sari, Y. P. (2019). An application of the GMM model on economic growth in Indonesia. Opcion, 90(2), 524-540.
  4. Aliasuddin, Syahnur, S., & Malia. (2020). Inflation and unemployment in Southeast Asian Countries: A Panel GMM application on Phillips curve. Regional Science Inquiry, upcoming.
  5. Asteriou, D., & Hall, S. G. (2016). Applied Econometrics (Third Edition ed.). London: Palgrave Macmillan.
  6. Bangsawan, S., MS, M., Ahadiat, A., Ribhan, Kesumah, F. S., & Febrian, A. (2021). Pengembangan Desa Wisata Melalui Pelatihan dan Pembinaan. Yumary: Jurnal Pengabdian kepada Masyarakat, 2(2), 79-90. doi:https://doi.org/10.35912/jpm.v2i2.615
  7. BI. (2020, May 18). Statistik Ekonomi Keuangan Indonesia. Retrieved from Bank Indonesia: www.bi.go.id
  8. Chekenya, N. S., & Dzingirai, C. (2020). The impact of the presence of autoregressive conditional heteroscedasticity (ARCH) effects on spurious regressions. Scientific African, 8, e00382. doi:https://doi.org/10.1016/j.sciaf.2020.e00382
  9. Ghysels, E., & Marcellino, M. (2018). Applied Economic Forecasting Using Time Series Methods. New York: Oxford University Press.
  10. Lutkepohl, H. (2007). New Introduction to Multiple Time Series Analysis. Berlin: Springer.
  11. Mikkael, R. H., Touana, H., & Takrim, M. (2020). PkM pelatihan peningkatan usaha mikro dalam mewujudkan smart businessmelalui smartphone di masa pandemi Covid-19. Yumary: Jurnal Pengabdian kepada Masyarakat, 1(1), 35-40. doi:https://doi.org/10.35912/jpm.v1i1.75
  12. Mishkin, F. S. (2019). The Economics of Money, Banking, and Financial Markets (Twelfth Edition, Global Edition ed.). London: Pearson Education Limited.
  13. Nguyen, T., Chaiechi, T., Eagle, L., & Low, D. (2020). Dynamic impacts of SME stock market development and innovation on macroeconomic indicators: A Post-Keynesian approach. Economic Analysis and Policy, 68, 327-347. doi:https://doi.org/10.1016/j.eap.2020.10.002
  14. Olayungbo, D. O. (2021). Global oil price and food prices in food importing and oil exporting developing countries: A panel ARDL analysis. Heliyon, 7(3), e06357. doi:https://doi.org/10.1016/j.heliyon.2021.e06357
  15. Qiyami, K. E., & Nilamsari, W. (2021). Pengembangan Kreativitas dan Produktivitas Siswa-Siswi Sekolah Dasar Islam Al-Azhar 8 Kembangan Jakarta Barat melalui Program Media Creative Class. Yumary: Jurnal Pengabdian kepada Masyarakat, 2(1), 39-49. doi:https://doi.org/10.35912/jpm.v2i1.390
  16. Roy, G., & Sharma, S. (2021). Measuring the role of factors on website effectiveness using vector autoregressive model. Journal of Retailing and Consumer Services, 62, 102656. doi:https://doi.org/10.1016/j.jretconser.2021.102656
  17. Siregar, M. I., Khamisah, N., Maryati, S., Pratiwi, T. S., L. D., H. F., . . . Kesuma, N. (2021). Sosialisasi dan Pelatihan Terkait Media Daring Google Classroom dan Google Form di Masa Pandemi Covid 19 pada Sekolah Dasar Negeri 23 Palembang. Yumary: Jurnal Pengabdian kepada Masyarakat, 2(2), 69-77. doi:https://doi.org/10.35912/jpm.v2i2.
  18. Stoian, A., & Iorgulescu, F. (2020). Fiscal policy and stock market efficiency: An ARDL Bounds Testing approach. Economic Modelling, 90, 406-416. doi:https://doi.org/10.1016/j.econmod.2019.12.023
  19. Sun, Y., Han, A., Hong, Y., & Wang, S. (2018). Threshold autoregressive models for interval-valued time series data. Journal of Econometrics, 206(2), 414-446. doi:https://doi.org/10.1016/j.jeconom.2018.06.009