Econometric Modeling and Forecasting of Interest Rates in Montenegro

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Bojan Pejović
Vesna Karadžić

Abstract

In contemporary conditions, the econometric modeling and forecast have a growing importance in both the development of several theoretic models and approaches that may be used and in the necessity of forecasting to make proper decisions by authorities for their making. The paper tests the possibility of applying the Box-Jenkins approach and vector autoregressive models for modeling and forecasting interest rates in Montenegro. Box-Jenkins approach and vector autoregressive models are one of multiple, yet the most used approaches and models used for forecasting time series values. Thee comparison of forecasting models determines the more superior model. The time series of the interest rate to be modeled and forecasted is a monthly weighted average lending interest rate of banks on new loans in the period from December 2011 to January 2018.


An example of the interest rate, which is extremely important in quite a bank-centric system in Montenegro, proved that the Box-Jenkins approach and VAR models may be used successfully for modeling and forecast. Moreover, the paper recommends the use of the Box-Jenkins approach and the assessed AR model for forecasting interest rate since it has better forecasting performances than the VAR model. Despite numerous limitations, primarily the inadequate statistical base, the AR model may find its application and help the decision-makers in the process of making economic decisions.


 


Keywords: forecasting, autoregressive models, interest rate forecasting, Box-Jenkins, VAR, AR.


 


 

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References

Ahmed, R., Vveinhardt, J., Ahmad, N., & Streimikiene, D. (2017). Karachi inter-bank offered rate (KIBOR) forecasting: Box-Jenkins (ARIMA) testing approach. E&M Economics and Management, 20(2), 188-198.
Alnaa, S. E., & Ahiakpor, F. (2011). ARIMA (autoregressive integrated moving average) approach to predicting inflation in Ghana. Journal of economics and international finance, 3(5), 328-336.
Box, G. E., & Jenkins, G. M. (1976). Time series analysis: Forecasting and control San Francisco. Calif: Holden-Day.
Carriero, A., Kapetanios, G., & Marcellino, M. (2009). Forecasting exchange rates with a large Bayesian VAR. International Journal of Forecasting, 25(2), 400-417.
Cologni, A., & Manera, M. (2008). Oil prices, inflation and interest rates in a structural cointegrated VAR model for the G-7 countries. Energy economics, 30(3), 856-888.
Dimitrios, A. (2006). Applied Econometrics: a modern approach using EViews and Microfit.
Dua, P. (2008). Interest rate modeling and forecasting in India (No. id: 1521).
Etuk, E. H. (2013). Monthly Nigerian Interbank Call Rates Modeling by Seasonal Box-Jenkins Approach. Journal of research in Marketing, 1(1), 22-29.
Fritzer, F., Moser, G., & Scharler, J. (2002). Forecasting Austrian HICP and its components using VAR and ARIMA models (No. 73).
Gerdesmeier, D., Roffia, B., & Reimers, H. E. (2017). Forecasting Euro Area Inflation Using Single-Equation and Multivariate VAR–Models. Folia Oeconomica Stetinensia, 17(2), 19-34.
Granger, C.W.J. and Newbold, P. (1974). Spurious Regression in Econometrics. International Journal of Social Science, Vol.5, No.1.
Hoa, T. T. (2017). Forecasting inflation in Vietnam with univariate and vector autoregressive models (No. BOOK). The Graduate Institute of International and Development Studies, International Economics Department.
Iqbal, M., & Naveed, A. (2016). Forecasting inflation: Autoregressive integrated moving average model. European Scientific Journal, 12(1), 83-92.
Koutsoyiannis, A. (1997). Theory of Econometrics: Introductory to Exposition of Econometric Methods, 2nd edition., Macmillan Publishers Ltd, London
Maddala, S.G. and Kim, I.M. (1998). Unit Root, Cointegration, and Structural Change, Cambridge University Press, New York
Moffat, I. U., & David, A. E. (2016). Modeling inflation rates in Nigeria: Box-Jenkins’ approach. International Journal of Mathematics and Statistics Studies, 4(2), 20-27.
Okafor, C., & Shaibu, I. (2013). Application of ARIMA models to Nigerian inflation dynamics. Research Journal of Finance and Accounting, 4(3), 138-150.
Phillips, P.C.B. (1987). Time Series Regression With Unit Root. Econometrica, Vol. 55, No. 2.
Radha, S., & Thenmozhi, M. (2006). Forecasting short term interest rates using ARMA, ARMA-GARCH and ARMA-EGARCH models. In Indian Institute of Capital Markets 9th Capital Markets Conference Paper.
Razak, N. A. A., Khamis, A., & Abdullah, M. A. A. (2017). ARIMA and VAR modeling to forecast Malaysian economic growth. Journal of Science and Technology, 9(3).
Salazar, E., & Weale, M. (1999). Monthly data and short‐term forecasting: an assessment of monthly data in a VAR model. Journal of Forecasting, 18(7), 447-462.
Sarantis, N., & Stewart, C. (1995). Structural, VAR and BVAR models of exchange rate determination: a comparison of their forecasting performance. Journal of Forecasting, 14(3), 201-215.
Seneviratna, D. M. K. N., & Shuhua, M. (2013). Forecasting the Twelve Month Treasury Bill Rates in Sri Lanka: Box Jenkins Approach. IOSR Journal of Economics and Finance (IOSR-JEF), 1(1).
Sims, C. A. (1980). Macroeconomics and reality. Econometrica: journal of the Econometric Society, 1-48.
Xue, D. M., & Hua, Z. Q. (2016). ARIMA Based Time Series Forecasting Model. Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering), 9(2), 93-98.
Yuan, C., Liu, S., & Fang, Z. (2016). Comparison of China's primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM (1, 1) model. Energy, 100, 384-390.
Zhang, H., & Rudholm, N. (2013). Modeling and forecasting regional GDP in Sweden using autoregressive models. Dalama University.