International Journal of Statistics and Applied Mathematics
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International Journal of Statistics and Applied Mathematics

2018, Vol. 3, Issue 2, Part G

Specification and stabiity of two equations vector autoregressive model for time series data analysis


Author(s): K Aswini, C Mani, P Srivyshnavi, S Venkata Ramaraju, Dr. B Ramanjuneyulu, G Mokesh Rayalu, P Balasiddamuni

Abstract: The most fertile areas of contemporary time series research concerns multiequation models. The vector autoregressive (VAR) model is the multivariate counter part of the univariate autoregressive model. A VAR model can be used in examining the relationships among a set of variables. The estimates of the parameters of the VAR model can be used for forecasting purposes. Forecasting with a VAR is a multivariable extension of forecasting using a simple autoregression. The VAR analysis tools such as Granger Causality tests, Impulse Response Analysis and Variance Decomposition models can be used in establishing relationships among economic variables; and in the formulation of structured economic models. In the present study a Two-Equations VAR model has been specified and stability conditions have been derived. Using Lag operators, the Two-Equations VAR model has been expressed in terms of Lag operators. This specification facilitates to study further inferential aspects of the VAR model for time series data analysis.

Pages: 538-548 | Views: 441 | Downloads: 7

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How to cite this article:
K Aswini, C Mani, P Srivyshnavi, S Venkata Ramaraju, Dr. B Ramanjuneyulu, G Mokesh Rayalu, P Balasiddamuni. Specification and stabiity of two equations vector autoregressive model for time series data analysis. Int J Stat Appl Math 2018;3(2):538-548.
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