US Dollar/IQ Dinar Currency Exchange Rates Time Series Forecasting Using ARIMA Model

Parzhin A. Mohammed; Sami A. Obed; Israa M. Ali; Dler H. Kadir

doi:10.24086/cuesj.v6n1y2022.pp12-19

Parzhin A. Mohammed Department of Statistics, College of Administration and Economics, Salahaddin University-Erbil, Kurdistan Region - F.R. Iraq. https://orcid.org/0000-0003-2998-9871
Sami A. Obed Department of Statistics, College of Administration and Economics, Salahaddin University-Erbil, Kurdistan Region - F.R. Iraq. https://orcid.org/0000-0002-2866-5886
Israa M. Ali Department of Statistics, College of Administration and Economics, Salahaddin University-Erbil, Kurdistan Region - F.R. Iraq. https://orcid.org/0000-0001-9642-4673
Dler H. Kadir 1 Department of Statistics, College of Administration and Economics, Salahaddin University-Erbil, Kurdistan Region - F.R. Iraq. 2 Department of Business Administration, Cihan University-Erbil, Iraq http://orcid.org/0000-0001-5196-4457

DOI: https://doi.org/10.24086/cuesj.v6n1y2022.pp12-19

Keywords: Exchange rate forecasting, US Dollar/IQ Dinar, time-series analysis, autoregressive integrated moving average model

Abstract

The use of currency exchange estimation as a tool for economic planning is being researched as a technique for gaining economic stability. The main purpose of this study is to use the ARIMA model to forecast monthly US dollar and IQ dinar exchange rates. The information was gathered from January 2010 to December 2020. We got the information from the website (sa.investing.com). The minimum value of Root Mean Square Error (RMSE) and the mean absolute error are used to select the optimal model (MAE). ARIMA was found to be the best model for the US Dollar/IQ Dinar series (2, 1,0). This is the forecasted meaning for the future of this exchange rate time series, which indicates a perpetual increase continuously in the next two years. Statgraphics version 15 was the statistical software package utilized to complete this project.

Downloads

Download data is not yet available.

Author Biographies

Parzhin A. Mohammed, Department of Statistics, College of Administration and Economics, Salahaddin University-Erbil, Kurdistan Region - F.R. Iraq.

I was awarded a B.Sc. from the University of Salahaddin in College of Administration & Economics \ Statistics Department in 2012. I also received a M.Sc. degree in applied statistics at Salahaddin University in 2016. I am an assistant Lecture In the Department of statistics.

Sami A. Obed, Department of Statistics, College of Administration and Economics, Salahaddin University-Erbil, Kurdistan Region - F.R. Iraq.

I graduated from Salahaddin University – Erbil in 2013 College of Administration & Economics \ Statistics Department from 2013 to 2016 worked Assistant Researcher in Statistics Department . In 2019I have earned master's degree in the Department of Statistics college of Administration and Economy, University of Salahaddin and through these years ( 2020-2021) I studied the third stage students Time series at the Department of Economics, and, so far, I am working as an assistant teacher in the Department of Statistics

Israa M. Ali, Department of Statistics, College of Administration and Economics, Salahaddin University-Erbil, Kurdistan Region - F.R. Iraq.

I was awarded a B.Sc. from the University of Salahaddin in College of Administration & Economics \ Statistics Department in 2013. I also received a M.Sc. degree in applied statistics at Salahaddin University in 2016. I am a Lecturer In the Department of Statistical & Informatics.

Dler H. Kadir, 1 Department of Statistics, College of Administration and Economics, Salahaddin University-Erbil, Kurdistan Region - F.R. Iraq. 2 Department of Business Administration, Cihan University-Erbil, Iraq

I was awarded a B.Sc. in Statistics from the University of Salahaddin in 2002. I also received a M.Sc. degree in applied statistics at Sulaymaniyah University in 2007. I was awarded a PhD from Sheffield University in 2018. My research interest in Bayesian inference, MCMC, Statistical Modeling, Quality control charts and Time series analysis

References

J. E. Buet, G. M. Barber and D. L. Rigby. Elementary Statistics for Geographers. 3rd ed. New York: The Guilford Press, 2009.

W. A. Fuller. Introduction to Statistical Time Series. 2nd ed. Canada: John Wiley and Sons, Inc., 1996.

A. K. Farhan and M. R. Fakhir. Forecasting the exchange rates of the Iraqi Dinar against the US Dollar using the time series model (ARIMA). International Journal of Engineering and Management Research, vol. 9, pp. 2394-6962, 2019.

E. P. Clement. Time series approach to forecasting rate analysis between Nigeria Naira and the U.S dollar. International Journal of Statistics and Applications Statistics, vol. 4, no. 1, pp. 76-84, 2014.

D. H. Kadir. Time series modeling to forecast on consuming electricity: A case study analysis of electrical consumption in Erbil city from 2014 to 2018. Journal of Al Rafidain University College, vol. 46, pp. 473-485, 2020.

G. E. P. Box and G. M. Jenkins. Time Series Analysis Forecasting and Control. 4th ed. USA: Library of Congress Catalog Card Number. 76-8713, 1976.

P. J. Brockwell and R. A. Davis. Time Series: Theory and Methods. 2nd ed. USA, New York: Springer Science and Business Media, LLC, 233 Spring Street, 2006.

H. Lutkepohul and M. Kratzig. Applied Time Series Econometrics. New York: The United States of America by Cambridge University Press; 2004.

J. Cryer and K. Chan. Time Series Analysis with Applications. 2nd ed. USA, New York: Springer Science and Business Media, LLC, 233 Spring Street, 10013, 2008.

P. J. Brockwell and R. A. Davis. Introduction to Time Series and Forecasting. 2nd ed. New York: Springer-Verlag, Inc.; 2002.

R. S. Shumway and D. S. Stoffer. Time Series Analysis and its Applications with R Example. 3rd ed. USA, New York: Springer Science and Business Media, LLC, 233 Spring Street, 2011.

D. C. Montgomery, C. L. Jennings and M. Kulahci. Introduction to Time Series Analysis and Forecasting. 2nd ed. Canada, Hoboken, New Jersey: John Wiley and Sons. Inc., 2016.

W. Wei. Time Series Analysis: Univariate and Multivariate Methods. 2nd ed. USA: Greg Tobin, 2006.

A. Omer, H. Blbas and D. Kadir D. A comparison between brown’s and holt’s double exponential smoothing for forecasting applied generation electrical energies in Kurdistan Region. Cihan University Erbil Scientific Journal, vol. 5, no. 2, pp. 56-63, 2021.

T. Cipra. Time Series in Economics and Finance. Nature, Switzerland, AG, 2020.

G. E. P. Box, G. M. Jenkins and G. C. Reincel. Time Series Analysis Forecasting and Control. 3rd ed. Hoboken, New Jersey: Prentice-Hall, Inc., 1994.

R. A. Yaffee and M. McGee. Introduction to Time Series Analysis and Forecasting with Applications of SAS and SPSS. Tokyo, Toronto: Academic Press, Inc.; 1996.