International Journal of Scientific Engineering and Technology

Open Access Open Access  Restricted Access Subscription Access

Modeling the Relationship Between Exchange Rate and Balance of Trade Components in Rwanda (2005-2012)


Affiliations
1 Jomo Kenyatta University of Agriculture and Technology, Kenya
 

Exchange rate stability affects foreign investments, price stability and stable economic growth. Rwandan currency has depreciated overtime. General objective of this study was to model the relationship between exchange rate and BOT in Rwanda. Specific objectives were to test for the stationarity of exchange rate and BOT components, to determine the relationship between exchange rate and BOT components and to investigate the effects of BOT components on exchange rate in Rwanda. The study utilized monthly time series data from 2005 to 2012 sourced from BNR and NISR. The study carried out ADF and PP unit ischolar_main tests, Johansen cointegration test, Quantile regression and impulse response function tests. The results of ADF and PP tests revealed partial stationarity at level and full at first difference. Johansen cointegration tests revealed a long run relation exists between exchange rate and BOT components. Quantile regression results revealed positive significant effect of BOT components on exchange rate. The impulse response results also confirmed the same. The study recommends adoption of import reduction strategies and export expansion strategies.

Keywords

Exchange Rate, BOT, Exports, Imports (C.I.F), Imports (F.O.B), Quantile Regression.
User
Notifications
Font Size

  • i. Atif et al (2012). Determinants of Australian Exchange Rate. Munich Personal RePEc Archive Paper No. 42309. pp 1-17

  • ii. BABBIE, E., 2010. The practice of social research. Belmont, CA; : Wadsworth. (301.072_BAB)

  • iii. Beatrice Mkenda(2001). Long-run and Short-run determinants of Real Exchange Rate in Zambia. Working Papers in Economics no 40. Goteborg University, Sweden.

  • iv. Brooks, C. (2008). Introductory Economics for Finance. Cambridge: Cambridge University Press

  • v. Huang et al (2017). Quantile Regression models and their Applications. Journal of Biometrics and Biostatistics, pp 354.

  • vi. Collier, P. and V. Joshi (2004) 'Exchange Rate Policy in Developing Countries', Oxford Review of Economic Policy: 94-113.

  • vii. Damodar N. Gujarati(2003). Basic Econometrics. Economic Series. Mc Graw Hill Publishers

  • viii. Danga, K. (2016). Determinants of Nominal exchange rate in Kenya. International Journal of Economics, Commerce and Management, IV(6), 137-172.

  • ix. Dimitrov, P. (2004, August 6). Differences between Balance of Payments and Foreign trade Statistics. Luxenbourg: European Communities.

  • x. Duasa, J. (2007). Determinants of Malaysian Trade Balance: An ARDL Bound Testing Approach. Journal of Economic Cooperation, 28(3), 21-40.

  • xi. Edwards, S. (2002). Capital Mobility, Capital Controls, and Globalization in the Twenty First Century. The Annals of the American Academy of Political and Social Science, 579, 261-70.

  • xii. Engel and Granger (1987). Cointegration and Error Correction: Representation ,Estimation and Testing. Econometrica.

  • xiii. Guechari, Y. (2012) 'An Empirical Study on the Effects of Real Effective Exchange Rate on Algeria’s Trade Balance', International Journal of Financial Research 3(4): p102.

  • xiv. Gujarati, D.N. (2003) Basic Econometrics. (4th Edition edn) McGraw Hill.

  • xv. Hacker, R.S. and A. Hatemi (2004) 'The Effect of Exchange Rate Changes on Trade Balances in the Short and Long Run', Economics of transition 12(4): 777-799.

  • xvi. Hjalmarsson, E., and P. Osterholm, 2007. Testing for Cointegration Using the Johansen Methodology when Variables are Near-Integrated, IMF Working Paper 07/141, International Monetary Fund.

  • xvii. International Monetary Fund (2005) “Exchange Rate Variability and World Trade”, IMF Occasional Paper 2

  • xviii. Jha, R. (2003) Macroeconomics for Developing Countries. (2nd edn) New York, USA: Routledge

  • xix. Johansen and Joselius (1990). Maximum likelihood estimation and inference on cointegration with application to the demand for money. Oxford Bulletin of Economics and Statistics

  • xx. Mahmud, S.F., et al. (2004). Testing Marshall Lerner Condition: a Non- Parametric Approach. Applied Economics Letters, 11, 231-236.

  • xxi. Mankiw, N.G. (2003) Macroeconomics. (5th Edition edn) New York: Worth Publishers

  • xxii. Nelson G.O. (2013). Theoretical Approaches to the understanding of Africans politics and challenges of development. European Center for Research Training and Development UK, 44-52.

  • xxiii. Odhiambo, Lyk (2015, 7). The Determinants Of Long-Run Real Exchange Rates In South Africa:A Fundamental Equilibrium Approach. UNISA Economic Research working Paper, pp. 1-26.

  • xxiv. Onafowora, O. (2003) 'Exchange Rate and Trade Balance in East Asia: Is there a J-Curve', Economics bulletin 5(18): 1-13.

  • xxv. Rucha and Burange(2013). Determinants of Indias Real Exchange Rate.An ARDL Approach. Mumbai: University of Mumbai.

  • xxvi. Shao, Z. (2009) Exchange Rate Changes and Trade Balance: An Empirical Study of the Case of Japan. SINGAPORE MANAGEMENT UNIVERSITY (SINGAPORE).

  • xxvii. Shukla and Amos (2017). Effect of Government Expenditure on Economic Growth in Rwanda(2005-2015). International Journal of Economic, Commerce and Management.

  • xxviii. Simbi, E. (2016). Empirical Analysis of determinants of Exchange rate in Rwanda(2000-2015) . Journal of Economics and Sustainable development, 7(18), 169-184.

  • xxix. Sunil Kumar. Determinants of Real Exchange Rate in India: An ARDL Approach, Reserve Bank of India Occasional Papers. 2010 , 3(1).

  • xxx. World Bank, (2011). Rwanda Economic Update. Kigali: Spring Edition.

  • xxxi. Wooldridge, Jeffrey M. (2003): Introductory Econometrics: A Modern Approach, 2nd edition, South Western College Publishing.


Abstract Views: 158

PDF Views: 0




  • Modeling the Relationship Between Exchange Rate and Balance of Trade Components in Rwanda (2005-2012)

Abstract Views: 158  |  PDF Views: 0

Authors

Ndayambaje Godefroid
Jomo Kenyatta University of Agriculture and Technology, Kenya
Joseph Mung’atu
Jomo Kenyatta University of Agriculture and Technology, Kenya
Ndengo Mercel
Jomo Kenyatta University of Agriculture and Technology, Kenya

Abstract


Exchange rate stability affects foreign investments, price stability and stable economic growth. Rwandan currency has depreciated overtime. General objective of this study was to model the relationship between exchange rate and BOT in Rwanda. Specific objectives were to test for the stationarity of exchange rate and BOT components, to determine the relationship between exchange rate and BOT components and to investigate the effects of BOT components on exchange rate in Rwanda. The study utilized monthly time series data from 2005 to 2012 sourced from BNR and NISR. The study carried out ADF and PP unit ischolar_main tests, Johansen cointegration test, Quantile regression and impulse response function tests. The results of ADF and PP tests revealed partial stationarity at level and full at first difference. Johansen cointegration tests revealed a long run relation exists between exchange rate and BOT components. Quantile regression results revealed positive significant effect of BOT components on exchange rate. The impulse response results also confirmed the same. The study recommends adoption of import reduction strategies and export expansion strategies.

Keywords


Exchange Rate, BOT, Exports, Imports (C.I.F), Imports (F.O.B), Quantile Regression.

References