Application of Auto-Regressive Distributed Lag Model (ARDL) Bound Test on Selected Macroeconomic Variables
Abstract
This study examined the application of Auto-regressive distributed lag model (ARDL) bound test on some selected macroeconomic variables spanning from 1981-2017 obtained from the statistical Bulletin of Central Bank of Nigeria (CBN). The data were analyzed using the E-views 9.0 software. F-statistic of 5.9167 was found to be higher than the critical value of 3.79 in the Lower Bound I(0) and 4.85 in the Upper bound I(1) at the 5 % level, thus null hypothesis was rejected. ARDL (1, 2, 0) was found to be the best fit model for showing a long-run and short-run relationship between Gross Domestic Product (GDP), Exchange rate, and Interest rate. There is a long-run relationship among GDP, Exchange rate, and Interest rate which means that the variables under study are co-integrated. Also, a unidirectional relationship running from exchange rate to GDP exist. The study recommends the use of supportive fiscal and monetary policies that will tighten the local currency market and provide a set of incentives aimed at removing anti-export bias barriers so as to promote exports and boost GDP, particularly non-oil exports and discourage import of consumer goods to stabilize the exchange rate.
Keywords: ARDL Bound test; Gross Domestic Product; Exchange rate; Macroeconomic Variables; Interest rate.
JEL Codes: E06; O2; O4
Full Text:
PDFReferences
Acha, C.K., & Amalahu, C. C. (2017). Interaction between some selected economic variance and their implication. Journal of the Nigeria Association of Mathematical Physics, 43, 285–292.
Arnold, A., Liu, Y., & Abe, N. (2007). Temporal Causal Modeling with Graphical Granger Methods. KDD’07 Proceedings of the 13th ACM SIGKDD International Conference Knowledge Discovery and Data Mining. New York, NY: ACM .
Bawa, S., Abdullahi, I. S., & Adamu, I. (2016). Analysis of Inflation Dynamics in Nigeria. CBN Journal of Applied Statistics, 7(1), 255–276.
Chen, P., & Hiao, C. Y. (2010). Looking behind Granger Causality. MPRA paper No: 24859.
Chu, T., & Glymlour, C. (2008). Search for Additive Nonlinear Time Series Causal Models. Journal of Machine Learning Research, 9, 967–991.
Clarke, J. A., & Mirza, S. (2006). A Comparison of Some Common Methods for Detecting Granger Non-Causality. Journal of Statistical Computation and Simulation, 76, 207-231.
Dickey, D. A., & Fuller, W. A. (1979). Distribution of the Estimators for Autoregressive Time Series With A Unit Root. Journal of the American Statistical Association, 74(366a), 427–431.
Eichler, M., & Didelez, V. (2007). Causal Reasoning in Graphical Time Series Models. Proceedings of the 23rd Conference on Uncertainty in Artificial Intelligence.
Entner D., & Hoyer, P.O. (2010).On Causal Discovery from Time Series Data Using FCI. Proceedings on the 5th European Workshop on Probabilistic Graphical Models (PGM). Helsinki, Finland.
Erdal, G., H. Erdal., & K. Esengun. (2008). The causality between Energy Consumption and Economic Growth in Turkey. Energy Policy, 36, 3838–3842.
Gartner, M. (2010) Macroeconomics. 3rd ed. USA: Pearson Education, 83–88.
Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-Spectral Methods.Econometrica.37, 424–435.
Granger, C. W. J. (1980). Testing for Causality. A Personal Viewpoint. Journal of Economic Dynamic and Control, 2(4), 329–352.
Granger, C. W. J. (1988). Some Recent Developments in a concept of Causality. Journal of Econometrics, 39(1),199–211.
Gujarati, D. N. (2004). Basic Econometrics. 4th Edition, McGraw-Hill Companies.
Haufe, S., Muller, K.R., Nolte, G., & Kramer, N. (2010). Sparse Causal Discovery in Multivariate Time Series. JMLR Workshop and Conference Proceedings, 6, 97-106. NIPS 2008 workshop on causality.
Hlavackova-Schlinder, K., Palvus, M., Vejmelka, M., & Bhattacharya, J. (2007). Causality detection based on information-theoretic approaches in time series analysis. Physics Reports, 441, 1–46.
Johansen, S. & K. Juelius (1990). Maximum Likelihood Estimation and Inference on Cointegration with Applications to Demand for Money. Oxford Bulletin of Economics and Statistics 52, 169–210.
Mohammed, Y. & Nishida T. (2010). Mining Causal Relationships in Multidimensional Time Series. Studies on Computational Intelligence, 260, 309-338.
Moneta, A., Chalb, N., Entner, D., & Hoyer, P. (2011). Causal Search in Structural Vector Autoregressive Models. JMLR: Workshops and Conference Proceedings. 12, 95– 118.
Musa, E. S., Yohanna, P. (2017). Exchange Rate Dynamics, Inflation and Economic Growth: Empirical Evidence from Turkish Economy. Journal Of Humanities And Social Science (IOSR-JHSS), 22(9), 42–49.
Pearl, J. (2012). Correlation and Causation-the Logic of Co-habitation. Written for the European Journal of Personality, Special Issue, 1–4.
Pesaran, M. H., & Shin, Y. (1999). An Autoregressive Distributed Lag Modelling Approach to Cointegration Analysis. Econometrics and Economic Theory in the 20th Century: The Ragnar Frisch Centennial Symposium, Strom, S. (ed.) Cambridge University Press.
Pesaran, M. H., Shin, Y., & Smith, R. J. (2001), Bounds Testing Approaches to the Analysis of Level Relationship. Journal of Applied Econometrics. 16(3), 289–326.
Shajoaie, A., & Michailidis, G. (2010). Discovering Graphical Granger Causality Using the Truncating Lasso Penalty. Bionformatics, 26(18), i517–i523.
Swanson, N. R., & Grenger, C.W.J. (1997). Impulse Response Functions Based on Caisal Aproach to Residual Orthogonalization in Vector Autoregressions, Journal of the Amrican Statistical Association, 92, 357–367.
Toda, H. Y., & Phillips, P.C.B.. (1994). Vector Autoregression and Causality: A Theorical Overview and Simulation Study. Econometric Reviews, 13, 259–285.
Toda, H, Y., & Yamamoto, T. 1995. Statistical Inference in Vector Autoregressions with Possibly Integrated Processes. EconPapers. 66. 225–250.
Zou, C., Ladroue, C., Guo, S., & Feng, J. (2010). Identifying Interactions in the Time and Frequency Domains in Local and Global Networks – A Granger Causality Approach. BMC Bioinformatics, 11, 337.
DOI: http://dx.doi.org/10.17977/um051v1i22018p79-86
Refbacks
- There are currently no refbacks.
This journal is indexed by:
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.