Detection of Non-Linearity in the Time Series Using BDS Test
Science Journal of Applied Mathematics and Statistics
Volume 3, Issue 4, August 2015, Pages: 184-187
Received: May 7, 2015;
Accepted: Jun. 16, 2015;
Published: Jul. 6, 2015
Views 7811 Downloads 408
Akintunde, M. O., Department of Statistics, School of applied Sciences, Federal Polytechnic, Ede, Osun State, Nigeria
Oyekunle, J. O., Department of Statistics, School of applied Sciences, Federal Polytechnic, Ede, Osun State, Nigeria
Olalude G. A., Department of Statistics, School of applied Sciences, Federal Polytechnic, Ede, Osun State, Nigeria
Follow on us
The need to determine the status of the series is a very important issue that must be addressed before embarking on the statistical analysis of such series; this paper therefore, examines the status of the commercial bank savings in Nigeria. From the analysis we discovered that at level the series was not stationary as shown in figure 1, however at the first difference (figure 2) the series was stationary, so also the unit root test applied shows that at level the series was not stationary (table 1) but at first difference it was stationary (table 2) and this actually paved way for the application of Brock- Dechert-Scheinkman (table 3) test which actually revealed that this series could be best estimated by the use of non-linear model as the null hypothesis of linearity of the series was out rightly rejected and the alternative was accepted. The importance of this result lies on the fact that it guides against model misspecification as using linear model to estimate the parameter of the non-linear model will result in model judgmental error.
Stationary, Unit Root, BDS Test, Linear Model, Non-Linear Model, Bank Savings
To cite this article
Akintunde, M. O.,
Oyekunle, J. O.,
Olalude G. A.,
Detection of Non-Linearity in the Time Series Using BDS Test, Science Journal of Applied Mathematics and Statistics.
Vol. 3, No. 4,
2015, pp. 184-187.
Abe, S. I. (1985) Assessment of Bank Performance in Nigeria: Bank examiner view point. Developing a healthy Banking System in Nigeria. Papers and Proceedings of the 1985 Bank Directors’ Seminar FITC, Yaba Lagos
Akintunde M. O., Shangodoyin D. K. and Kgosi P. M (2013): Measuring the forecast performance of GARCH and Bilinear-GARCH models in time series data. American Journal of Applied Mathematics
Baltrop J. C., (1992) Banking Institutions in Developing Market Interpreting Financial Statements, The World Bank, Washington D.C.
Brock, W. A., W. Dechert, & J. Scheinkman. (1996). A test for independence based on the correlation dimension. Working paper, University of Winconsin at Madison, University of Houston, and University of Chicago.
Guglielmo M.C. (2005). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics 31, 307-327
Higgins, M. (1998). Demographics, National Savings and International Capital Flows. International Economic Review, No. 39 Pp 343-369.
Hsieh, D. A. (1993). Implications of nonlinear dynamics for financial risk management. Journal of Financial and Quantitative Analysis. 28(1): 41 – 64.
Kwiatkowski, D., Phillips, P.C.B., Schmidt, P., and Shin, Y. (1992) “Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we sure that economic time series have a unit root?”, Journal of Econometrics, 54, 159-178.
Lin, K. (1997) “The ABC’s of BDS.” Journal of Computational Intelligence in Finance. 97(July/August): 23 – 26.
Loayza N., Schmidt H. K., Serven L., (1999). What Drives Private Savings across the World? Central Bank of Chile Working Paper, No. 47.
Loayza, N , K. Schimdt H.K, and Serven L., (2000). What Drives Private Saving across the World? Review of Economics &Statistics, Vol. 82 No. 2 Pp 165-181.
Mohsin H., Zeshan A., Muhammad B. (2006). The Impact of Demography, Growth, and Public Policy on Household Saving (A case study of Pakistan). Asia-Pacific Development Journal Vol. 13 No 2.
Nwadibia G., (1992). Theory of Money and Banking Freeman Production, Ibadan.