Main Article Content

Abstract

This classical regression model is designed to handle the relationship between stationary variables and should not be applied to non-stationary series. A time series data is said to be stationary if the mean, variance, and covariance remain constant over time. The problem associated with non-stationary variables, and often encountered by researchers when dealing with time series data, is spurious regression. A clear indicator of false regression is the low Durbin-Watson statistic but has a higher coefficient of determination (R2). Therefore, before doing modeling or forecasting using time series data, it is very important to do a stationary test. In this study, we use inflation data in the City of Bukittinggi from January 2014 to December 2019 as a case study. The data shows an uptrend and correlated error terms. Empirical results show that inflation data in Bukittinggi City is a stationary series.

Keywords

stationary, non autocorrelation, Phillips-Peron Test, Augmented Dickey Fuller Test, Inflasi

Article Details

How to Cite
1.
Roza A, Violita ES, Aktivani S. Study of Inflation using Stationary Test with Augmented Dickey Fuller & Phillips-Peron Unit Root Test (Case in Bukittinggi City Inflation for 2014-2019). Eksakta [Internet]. 2022Jun.30 [cited 2022Jul.5];23(02):106-1. Available from: https://eksakta.ppj.unp.ac.id/index.php/eksakta/article/view/303

References

  1. Chauhan, R., Ali, H., & Munawar, N. A. (2019). Building performance service through transformational leadership analysis, work stress and work motivation (empirical CASE study in stationery distributor companies). Dinasti International Journal of Education Management and Social Science, 1(1), 87-107.
  2. Xu, Y., Du, B., Zhang, L., Cerra, D., Pato, M., Carmona, E., ... & Le Saux, B. (2019). Advanced multi-sensor optical remote sensing for urban land use and land cover classification: Outcome of the 2018 IEEE GRSS data fusion contest. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(6), 1709-1724.
  3. van Rijn, J. N., Holmes, G., Pfahringer, B., & Vanschoren, J. (2018). The online performance estimation framework: heterogeneous ensemble learning for data streams. Machine Learning, 107(1), 149-176.
  4. Kollmann, T., Stöckmann, C., & Linstaedt, J. W. (2019). Task conflict, narcissism and entrepreneurial capability in teams planning a business: A moderated moderation approach to explaining business planning performance. Journal of Small Business Management, 57(4), 1399-1423.
  5. Hang, T. T. B., Nhung, D. T. H., Huy, D. T. N., Hung, N. M., & Pham, M. D. (2020). Where Beta is going–case of Viet Nam hotel, airlines and tourism company groups after the low inflation period. Entrepreneurship and Sustainability Issues, 7(3), 2282.
  6. Lindner, T., Puck, J., & Verbeke, A. (2020). Misconceptions about multicollinearity in international business research: Identification, consequences, and remedies. Journal of International Business Studies, 51(3), 283-298.
  7. Angelina, S., & Nugraha, N. M. (2020). Effects of Monetary Policy on Inflation and National Economy Based on Analysis of Bank Indonesia Annual Report. Technium Soc. Sci. J., 10, 423.
  8. Anggraeni, D., & Irawan, T. (2018). Causality Analysis of Producer Price Index (PPI) and Consumer Price Index (CPI) in Indonesia. Jurnal Ekonomi dan Kebijakan Pembangunan, 7(1), 60-77.
  9. Daşdemir, E. (2022). A New Proposal for Consumer Price Index (CPI) Calculation and Income Distribution Measurement by Income Groups. Journal of Economy Culture and Society, (65), 395-414.
  10. Akin, A. C., Cevrimli, M. B., Arikan, M. S., & Tekindal, M. A. (2019). Determination of the causal relationship between beef prices and the consumer price index in Turkey. Turkish Journal of Veterinary & Animal Sciences, 43(3), 353-358.
  11. Singla, C., Sarangi, P. K., Singh, S., & Sahoo, A. K. (2019). Modeling Consumer Price Index: An Empirical Analysis Using Expert Modeler. Journal of Technology Management for Growing Economies, 10(1), 43-50.
  12. Meyer, D. F., & Habanabakize, T. (2018). Analysis of Relationships and Causality between Consumer Price Index (CPI), the Producer Price Index (PPI) and Purchasing Manager’ s Index (PMI) in South Africa. Journal of Economics and Behavioral Studies, 10(6 (J)), 25-32.
  13. Fauzan, M., Wanto, A., Suhendro, D., Parlina, I., Damanik, B. E., Siregar, P. A., & Hidayati, N. (2018). Epoch Analysis and Accuracy 3 ANN Algorithm Using Consumer Price Index Data in Indonesia. In 3rd International Conference of Computer, Environment, Agriculture, Social Science, Health Science, Engineering and Technology (pp. 1-7).
  14. Dynamic Table on the Website of the Central Statistics Agency of West Sumatra. https://sumbar.bps.go.id/subject/3/inflasi.html#subjekViewTab3.
  15. Rangapuram, S. S., Seeger, M. W., Gasthaus, J., Stella, L., Wang, Y., & Januschowski, T. (2018). Deep state space models for time series forecasting. Advances in neural information processing systems, 31.
  16. Dau, H. A., Bagnall, A., Kamgar, K., Yeh, C. C. M., Zhu, Y., Gharghabi, S., ... & Keogh, E. (2019). The UCR time series archive. IEEE/CAA Journal of Automatica Sinica, 6(6), 1293-1305.
  17. Smyl, S. (2020). A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting. International Journal of Forecasting, 36(1), 75-85.
  18. Munir, M., Siddiqui, S. A., Dengel, A., & Ahmed, S. (2018). DeepAnT: A deep learning approach for unsupervised anomaly detection in time series. Ieee Access, 7, 1991-2005.
  19. Siami-Namini, S., Tavakoli, N., & Namin, A. S. (2018, December). A comparison of ARIMA and LSTM in forecasting time series. In 2018 17th IEEE international conference on machine learning and applications (ICMLA) (pp. 1394-1401). IEEE.
  20. Torres, J. F., Hadjout, D., Sebaa, A., Martínez-Álvarez, F., & Troncoso, A. (2021). Deep learning for time series forecasting: a survey. Big Data, 9(1), 3-21.