IIARD International Institute of Academic Research and Development

INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND MATHEMATICAL THEORY (IJCSMT )

E-ISSN 2545-5699
P-ISSN 2695-1924
VOL. 8 NO. 1 2022
DOI: https://www.doi.org/10.56201/ijcsmt.v8.no1.2022.pg58.72

---

Panel Auto-Regressive Distributed Lag (PARDL) Modeling of Exchange Rate in Oil Driven Economies in Africa

DA-Wariboko, Asikiye Yvonne, Essi I. Didi & Etuk, H. E

---

Abstract

This study on Panel Auto-Regressive Distributed Lag (PARDL) Modeling of Exchange Rate in Oil Driven Economies in Africa, modeled exchange rate, lending interest rate and inflation for six oil producing countries in Africa within 1980 to 2019. These countries are listed as; Algeria, Angola, Egypt, Gabon, Libya and Nigeria. This paper is aimed to build an appropriate Panel Auto-Regressive Distributed Lag (P/ARDL) model for the listed variables for the selected countries, evaluate the pattern of relationship between exchange rate and other economic variables such as lending interest rate and inflation rate in the selected countries. The model is adequate for the study. The exchange rate of the local currencies of the selected countries per USD is the dependent variable, while the explanatory variables are the lending interest rate and inflation. The data used for this research is secondary in nature and the result from the estimation indicates that both lending interest rate and inflation are statistically significant with the same p-value 0.000, meaning that exchange rate is determined by both variables across all the selected six countries that make up the panel. The model obtained will be used to formulate and recommend suitable policy/policies that will improve economic growth in the selected countries. The study therefore recommends that since exchange rate is determined by lending interest rate and inflation, monetary authorities in the region should pursue stabilizing lending interest rate and inflation as swings in these determinants will cause threat in stabilizing exchange rate hence affect economic growth.

keywords:

Panel Auto-Regressive Distributed Lag, Exchange Rate, Lending Interest Rate, Inflation, Oil Driven Economies.

---

References:

Adelakun, O. J., & Ngalawa, H. (2020). Modelling exchange rate pass-through: A model of oil
prices and asymmetric exchange rate fluctuations. Journal of Economic and Financial
Sciences, 13(1), 10.

Aftab, M., Syed, K. B. S., & Katper, N. A. (2017). Exchange-rate volatility and Malaysian-Thai
bilateral industry trade flows. Journal of Economic Studies.

Bingilar, P. F., Edoumiekumo, A. R., Kpolode, O. P., & Nkak, P. E. (2020). Accounting
Implications of Oil Price, Exchange Rate and Unemployment on Economic Growth in
Nigeria. Journal of Business and Management, 22(7), 01-06.

Dornbusch, R. Fischer, S & Startz , R. (2004), Macroeconomic. New York Worth Publisher,
Economics. Chicago. University of Chicago Press, 1953,(157-203).

Engle, R. F. & Granger C. J. (1987). Co-integration and Error-correction Representation,
Estimation and Testing, Econometrica, 55 251-278.

Fowowe, B. (2014). Modelling the oil price–exchange rate nexus for South Africa. International
Economics, 140, 36-48.

Hassler, U., & Wolters F. (2006). Autoregressive Distributed Lag Models and Cointegration.
Altgemeines Statistischers Archvi. 90(1):59-74.

Insah, B. & Chiaraah, A. (2013). Sources of Real Exchange Rate Volatility in Ghanaian
Economy. Journal of Economics and International Finance, 5(6), 232-238.

Nwafor Godwin O. , Etuk Ette H. , Emeka Amos (2018). Multivariate Transfer Function
Modeling. An Application. Researchjournali’s Journal of Mathematics Vol. 5 | No. 5
July 2018 ISSN 2349-5375

Onwukwe, C. E. and Nwafor, G.O. (2014). A Multivariate Time series modeling of major
economic indicators in Nigeria. American Journal of Applied Mathematics and statistics, 2
(6), 376 – 385.

Pesaran, M. H., & Smith, R. (1995). Estimating long-run relationships from dynamic
heterogeneous panels. Journal of econometrics, 68(1), 79-113.

Pesaran, M. H., Shin, Y. & Smith, R. J. (2001). Bounds Testing Approaches to the Analysis of
Level Relationships, Journal of Applied Economics, 1(6), 289-326.

Shrestha, M. B., & Bhatta, G. R. (2018). Selecting appropriate methodological framework for
time series data analysis. The Journal of Finance and Data Science, 4(2), 71-89.

Wa-Ntita, K.S. (2015). The determinants of access to health care services: Empirical evidence
from African countries. https://researchspace.ukzn.ac.za/bitstream/handle/10413/14238/

DOWNLOAD PDF

---

Back