Multivariate Time Series Modelling with Seasonal Univariate Components; Evidence from Nigeria GDP

Main Article Content

Taofikat Abidemi Azeez
Yusuf Olufemi Olusola
Hamzat Kayode Idris
Salawu Monsuru Micheal

Abstract

The patterns of GDP variables are graphically examined using time plot presented the time plot for the GDP variables concerning time presented a combined single time plot for all the considered GDP variables. The relationship, as well as the degree of relationship between/among the GDP variables, was further revealed by computing the pairwise correlation. Based on the output, each variable when crossed classified with itself have a strong positive correlation with an output of (1), while pairwise correlation reveals a positive figure with the least estimate being (0.3149), this implies that for all the variables there exist a positive correlation. All the pairwise relationship reveals a strong positive association with all the estimates revealing a value between (0.8-0.9) except ‘trade and industry' that shows a positive relationship but not strong with an estimate of (0.3149). The initial test in fitting a time series model is to examine the series for stationarity. The Augmented Dickey-Fuller test revealed that ‘Agriculture’, ‘Construction’, and Services’” satisfies the requirement of stationarity while the series ‘industry and “Trade” are non-stationary initially but later became stationary after the application of the first difference transformation which was confirmed after the application of the ADF test to the first differenced series. The Johansen co-integration's Trace test was employed to determine the order of co-integration and it was revealed that the series are cointegrated hence presentation of the equation of integration. We presented the lag length estimation criteria which revealed that the lag length of order 5 is appropriate for the VAR model as suggested by Akaike Information Criteria (AIC), Hannan-Quinn (HQ) Information Criteria, Schwarz Information Criteria (SC). The VAR(5) model was fitted for all the considered GDP variables.

Keywords:
Akaike Information Criteria (AIC), Hannan-Quinn (HQ) information criteria, Schwarz Information Criteria (SC), Bayesian Information Criterion (BIC)

Article Details

How to Cite
Abidemi Azeez, T., Olusola, Y. O., Idris, H. K., & Monsuru Micheal, S. (2019). Multivariate Time Series Modelling with Seasonal Univariate Components; Evidence from Nigeria GDP. Asian Journal of Probability and Statistics, 5(4), 1-20. https://doi.org/10.9734/ajpas/2019/v5i430140
Section
Review Article

References

Iwok IA. Modeling multivariate time series with univariate seasonal components. Journal of Statistical and Econometric Methods. 2018;5(4):39-61.

ISSN: 1792-6602 (print), 1792-6939 (online) Scienpress Ltd, 2018.

Akpokodje G. Exchange rate volatility and external trade: The experience of selected African countries. In. Adeola Adenikinju, Dipo Busari and Sam Olofin (ed.) Applied Econometrics and Macroeconomic Modelling in Nigeria. Ibadan University Press; 2009.

National Bureau of Statistics Publication on Nigerian Gross Domestic Product Report: Quarter One. 2015;05.

Raphael D, Daniel J. Comparative study of Univariate and Bivariate time series modeling. Advances in Statistics. 2016;4(5):31-38.

United Nations Development Programme. Human Development Reports. New York: Palgrave Macmillan; 2011.

Overview of the Nigerian economy (www.wikipedia.en); 2017.

Lutkepol H. New introduction to multiple time series analysis. Springer Berlin Heidebelg New York; 2005.

ISBN: 3-540-40172-5.

SPIN: 10932797.

Abdul AF, Marwan MA. The effect of interest rate, inflation rate and GDP on real economic growth rate in Jordan. Asian Economic and financial Review. 2013;5:34-41.

Sultan ZA, Haque MI. The estimation of the co-integration relationship between Growth, Domestic Investment and Export in Indian economy. International Journal of Economics and Finance. 2011; 3(4):226-232.

Rogers Simon, Sedghi Ami. How Fitch, Moody's and S&P rate each country's credit rating. The Guardian. London; 15 April, 2011.

Olomola Ade S. Strategies for managing the opportunities and challenges of the current agricultural commodity booms in SSA. In seminar papers on managing commodity booms in Sub-Saharan Africa: A Publication of the AERC Senior Policy Seminar IX. African Economic Research Consortium (AERC), Nairobi, Kenya; 2007.

Verma R, Wilson E. Savings, investment, foreign inflows and economic of the Indian economy 1950-2001. Economics Working Paper Series, University of Wollongong; 2005.

Adenikinju A, Alaba O. Energy use and productivity performance in the Nigerian manufacturing sector (1970-90). Centre for econometric and allied research and department of economics; University of Ibadan; 2000.

Central Bank of Nigeria: Annual Report and Statement of Account; 2007.

Central Bank of Nigeria Statistical Bulletin, Various Issues; 2016.

Murphy M, Teddy S. Comparative study between non seasonal linear and multivariate time series model. International Journal of Physical Sciences. 2013;6(3):34-41.