Missing Values Estimation for a Stable Bivariate Autoregressive Process

I.A. Iwok^1,

¹Department of Mathematics/Statistics, University of Port-Harcourt, P.M.B.5323, Port-Harcourt, Rivers State; Nigeria

Cite this paper

I.A. Iwok. Missing Values Estimation for a Stable Bivariate Autoregressive Process. American Journal of Applied Mathematics and Statistics. 2016; 4(3):67-73. doi: 10.12691/AJAMS-4-3-2

Abstract

This work proposed a method for the estimation of missing values in a stable bivariate autoregressive time series process. Missing observations were created at different positions in a stable bivariate series and the method was applied. Despite its ease of implementation, the obtained results suggested good performance of the method. The estimates obtained were compared with those of other existing methods. The result showed that the proposed method provides better estimates than the existing methods.

Keywords

VAR process, stability condition, selection criteria and missing values.

Copyright

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References

[1]	Giovanni, F. and Grassetti, (2011). Multidimensional time series model estimation in the presence of partially missing observations. Workshop SIG-annual conference paper, Vol.3, No. 3, pp. 28 -35.
[2]	Harvey A. and Koopman S.J. (2015). Messy time series ‘’A unified approach’’. Advances in Econometrics; Vol. 13, pp 103-143.
[3]	Lee S.M. and Robert S. (2008). Multivariate time series forecasting in incomplete environments. Technical report PARG 08-03. Robostics Research group. Department of Engineering Science, University of Oxford.
[4]	Lutkepol H. (2005): New introduction to multiple Time Series Analysis. Springer Berlin Heidebelg New York.
[5]	Patton, A.J. (2006). Estimation of multivariate models for time series of possibly different length. Journal of applied Econometrics: 21: 147-173.
[6]	Peterson, H. and Pederson, B. (2010). Handling missing values and Outliers in a purely random data. Journal of Mathematical Scieces. Vol.3, No. 7, pp. 37-41.
[7]	Rodrignes, P.C. and Carvalho, M. (2014). Spectral Modelling of time series with missing data. Centre for Mathematics and applications. Faculty of Science and Technology. Nova University of Lisbon; 2829-516 Caparica, Portugal.
[8]	Stoffer, D.S. (1986). Estimation and Identification of space-time ARMAX models in the presense of missing data. Journal of the American Statistical Association. Vol. 81, No. 395, theory and methods.
[9]	Zuur, A.F. et al (2015). Estimating common trends in multivariate time series using dynamic factor analysis. J. ana. In Stat. Vol.2, No. 5, pp. 19-24.