Non-cash Payment Transaction Projection Using ARIMAX : Efect of Calendar
DOI:
https://doi.org/10.20956/jmsk.v16i3.8546Keywords:
, ARIMAX, Calendar Variation, Non-cash PaymentAbstract
As the most Moslem country, economic activity in Indonesia is often parallel with the movement of Qamariah (lunar) calendar which is different with Gregorian calendar. Using calender variation, this research attempts to look for modified time series model for non-cash payment projection (forecast) aim. The result shows that calendar variation plays statistically significant role on non-cash payment, evidenced by significant payment in the month in which Eid Fitr occurs. The occurrence of Eid Fitr in the first and second week of the month is evidently characterized by increasing non-cash payment in one month earlier. The best model with highest accuracy for non-cash payment projection is ARIMAX(2,1,1) as it is able to capture the pattern, trend and fluctuation. It also suggests the peak of non-cash payment will be in December.
References
Goczek, L. dan Witkowski, B. 2015. Determinants of Card Payments. Applied Economics, vol. 48, no. 16, hal. 1530-1543.
Tazkiyyaturrohmah, R. 2018. Eksistensi Uang Elektronik sebagai Alat Transaksi Keuangan Modern. Muslim Heritage, vol. 3, no. 1, hal. 23-44.
Wang, Z. dan Wolman, A.L. 2016. Payment Choice and Currency Use: Insights from Two Billion Retail Transactions. Journal of Monetary Economics, vol. 84, hal. 94-115.
Wulandari, D., Soseco, T., dan Narmaditya, B.S. 2016. Analysis of the Use of Electronic Money in Efforts to Support the Less Cash Society. International Finance and Banking, vol. 3, no. 1, hal. 1-10.
Ozili, P.K. 2018. Impact of digital finance on financial inclusion and stability. Borsa Istanbul Review, vol. 18, no. 4, hal. 329-340.
Lai, Y. W. dan Windawati, A. 2017. Risk, return, and liquidity during Ramadan: Evidence from Indonesian and Malaysian stock markets. Research in International Business and Finance, vol. 42, hal. 233-241.
Setiawan, Suhartono, Ahmad, I.S., dan Rahmawati, N.I. 2015. Configuring calendar variation based on time series regression method for forecasting of monthly currency inflow and outflow in Central Java. AIP Conference Proceedings, vol. 1691, no. 1, id. 050024.
Anggraeni, W., Vinarti, R.A., dan Kurniawati, Y.D. 2015. Performance Comparisons between ARIMA and ARIMAX Method in Moslem Kids Clothes Demand Forecasting: Case Study. Procedia Computer Science, vol. 72, hal. 630-637.
Lin, H. L., Liu, L.M., Tseng, Y.H., dan Su, Y.W. 2011. Taiwan's International Tourism: A Time Series Analysis with Calendar Effect and Joint Outlier Adjustments. International Journal of Tourism Research, vol. 13, no. 1, hal. 1-16.
Ahmad, I.S., Setiawan, Suhartono, dan Masun, N.H. 2015. Forecasting of Monthly Inflow and Outflow Currency using Time Series Regression and ARIMAX: The Idul Fitri Effect. AIP Conference Proceedings, vol. 1691, no. 1, id. 050002.
Suharsono, A., Suhartono, Masyitha, A., dan Anuravega, A. 2015. Time Series Regression and ARIMAX for Forecasting Currency Flow at Bank Indonesia in Sulawesi Region. AIP Conference Proceedings, vol. 1691, no. 1, id. 050025.
Hutama, A.H., Akbar, S. dan Candra, M.Z.C. 2018. Medium Term Power Load Forecasting for Java and Bali Power System using Artifical Neural Network and SARIMAX. IEEE 5th International Conference on Data and Software Engineering (ICoDSE).
Wei, W.W.S. 2006. Time Series Analysis: Univariate and Multivariate Methods 2nd Edition. California: Addison-Wesley Publishing Company Inc.
Armstrong, J.S. 2007. Significance Tests Harm Progress in Forecasting. International Journal of Forecasting, vol. 23, no. 2 , hal. 321-327.
Box, G.E.P., Jenkins, G.M, dan Reinsel, G.C. 2008. Time Series Analysis, Forecasting and Control 4th Edition. New Jersey: John Wiley & Sons Inc.
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