Gurgul, Henryk and Suder, Marcin (2013): Modeling of Withdrawals from Selected ATMs of the “Euronet” Network. Published in: Managerial Economics , Vol. 13, No. 1 (2013): pp. 65-82.
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Abstract
This paper deals with the problem of withdrawals from Automated Teller Machines (ATMs), using daily data for selected ATMs installed by the Euronet network in the Polish provinces of Małopolska and Podkarpacie for the period from January 2008 to March 2012. The main aim of this paper is an estimation of the proper econometric models for withdrawals time series and attempt to forecast future demand on cash flow in ATMs in respect to their localization. This is necessary to establish a replenishment schedule. The results of computations suggest that models built on the basis of SARIMA methodology are useful tools for an modeling daily withdrawals time series. This kind of model can be applied independently of the localization of an ATM. The exercises for ex post data imply ex post forecast errors under 20%. This size of forecast errors is lower than the bias of actual replenishment scheduling.
Item Type: | MPRA Paper |
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Original Title: | Modeling of Withdrawals from Selected ATMs of the “Euronet” Network |
English Title: | Modeling of Withdrawals from Selected ATMs of the “Euronet” Network |
Language: | English |
Keywords: | ATMs, withdrawals, replenishment scheduling, SARIMA modeling |
Subjects: | G - Financial Economics > G0 - General G - Financial Economics > G0 - General > G00 - General |
Item ID: | 68598 |
Depositing User: | Dr Łukasz Lach |
Date Deposited: | 30 Dec 2015 09:02 |
Last Modified: | 01 Oct 2019 13:45 |
References: | [1] Amromin E., Chakravorti S., Debit card and cash usage: a cross-country analysis, Technical report, Federal Reserve Bank of Chicago, 2007. [2] Bell W.R., Hillmer S.C., Modeling Time Series with Calendar Variation, “Journal of the American Statistical Association” 1983, 78, pp. 526–534. [3] Boeschoten W.C., Cash management, payment patterns and the demand for money,“De Economist” 1998, 146, pp. 117–42. [4] Brentnall A.R., Crowder M.J., Hand, D.J., A statistical model for the temporal pattern of individual automated teller machine withdrawals, “Applied Statistics” 2008, 57, Part 1, pp. 43–59. [5] Brentnall A.R., Crowder M.J., Hand D.J., Predicting the amount individuals withdraw at cash machines using a random effects multinomial model, “Statistical Modelling” 2010, 10(2), pp. 197–214. [6] Carlsen M., Storgaard P.E., Dankort payments as a timely indicator of retail sales in Denmark, Danmarks Nationalbank Working Papers, No. 66, 2010. [7] Cleveland W. S., Devlin S.J., Calendar Effects in Monthly Time Series: Detection by Spectrum Analysis and Graphical Methods, “Journal of the American Statistical Association” 1980, 371, 75, pp. 487–496. [8] Cleveland W.P., Grupe M.R., Modeling time series when calendar effects are present, Applied Time Series Analysis of Economic Data, ed. A. Zellner, U.S. Department of Commerce, U.S. Bureau of the Census, Washington D.C., 1983, pp. 57–67. [9] Esteves P.S., Are ATM/POS Data Relevant When Now casting Private Consumption?, Banco de Portugal Working Paper, 25, 2009. [10] Findley D.F., Monsell B.C., Modeling Stock Trading Day Effects Under Flow Day-of-Week Effect Constraints, “Journal of Official Statistics” 2009, Vol. 25(3), pp. 415–430. [11] Findley D.F., Monsell B.C., Bell W.R., Otto M.C., Chen B.C., New capabilities and Methods of the X-12-ARIMA seasonal adjustment program, “Journal of Business and Economic Statistics” 1998, vol. 16(2), pp. 127–177. [12] Findley D.F., Soukup R.J., On the Spectrum Diagnostics Used by X-12-ARIMA to Indicate the Presence of Trading Day Effects after Modeling or Adjustment, “Proceedings of the American Statistical Association, Business and Statistics Section” 1999, pp. 144–149. [13] Findley D.F., Soukup R.J., Modeling and Model Selection for Moving Holidays, “Proceedings of the American Statistical Association, Business and Economics Statistics Section” 2000, pp. 102–107. [14] Findley D.F., Soukup R.J., Detection and Modeling of Trading Day Effects, in: ICES II: Proceedings of the Second International Conference on Economic Surveys, 2001, pp. 743–753. [15] Galbraith J.W., Tkacz G., Electronic Transactions as High-Frequency Indicators of Economic Activity, Bank of Canada Working Paper, 2007-58. [16] Gerdes G.R., Walton J.K., Liu M.X, Parke D.W., Trends in the Use of Payment Instruments in the United States, Federal Reserve Bulletin 91 (Spring), 2005, pp. 180–201. [17] Gurgul G., Suder M., Efekt kalendarza wypłat z bankomatów sieci Euronet, Zeszyt Naukowy nr 8, WSEI, Kraków 2012, s. 25–42. [18] Gurgul H., Suder M., Empiryczne własności wielkości dziennych wypłat z bankomatów na przykładzie sieci bankomatów firmy EURONET z terenu województw: małopolskiego i podkarpackiego, to appear. [19] Hansen P.R., Lunde A., Nason J.M., Testing the Significance of Calendar Effects, Working Paper 2005-2, Federal Reserve Bank of Atlanta, 2005. [20] Hand D.J., Blunt G., Prospecting for gems in credit card data,IMA “Journal of Management Mathematics” 2001, 12, pp.173–200. [21] Kufel T., Ekonometryczna analiza cykliczności procesów gospodarczych o wysokiej częstotliwości obserwowania, Wydawnictwo Naukowe Uniwersytety Mikołaja Kopernika, Toruń 2010. [22] Liu L.M., Analysis of Time Series with Calendar Effects, “Management Science” 1980, 26, pp. 106–112. [23] McElroy T.S., Holland S., A Nonparametric Test for Assessing Spectral Peaks, Research Report 2005-10, Statistical Research Division, U.S. Bureau of the Census, Washington D.C., 2005. [24] Rodrigues P., Esteves P., Calendar effects in daily ATM withdrawals, “Economics Bulletin” 2010, Vol. 30, no. 4, pp. 2587–2597. [25] Schmitz S., Wood G., Institutional Change in the Payments System and Monetary Policy, Routledge London, 2006. [26] Simutis R., Dilijonas D., Bastina L., Cash demand forecasting for ATM using Neural Networks and support vector regression algorithms, 20th International Conference, EURO Mini Conference, “Continuous Optimization and Knowledge-Based Technologies” (EurOPT-2008), Selected Papers, Vilnius May 20–23, 2008, pp. 416–421. [27] Snellman H., Viren M., ATM networks and cash usage, “Applied Financial Economics” 2009, 19 (10), pp. 841–851. [28] Takala K., Viren M., Impact of ATMs on the Use of Cash, “Communications and Strategies” 2007, No 66, pp. 47–61. [29] Young A.H., Estimating Trading-day Variation in Monthly Economic Time Series, Technical Paper 12, Bureau of the Census, 1965. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/68598 |