International Journal of Statistics and Applied Mathematics
2018, Vol. 3, Issue 1, Part F
Estimation of parameters of a multivariate time series var modelAuthor(s):
M Ramesh, P Vishnu Priya, P Srivyshnavi, G Madhusudan, K Aswini and P BalasiddamuniAbstract:
Multivariate Time Series model building involves five important steps namely, Identification Specification, Estimation and testing the hypotheses, Diagnostic checking and Forecasting. Estimation of parameters of multivariate Vector Autoregressive (VAR) model is more complicated than that of univariate autoregressive models. Under normality of the errors, Maximum likelihood estimation as well as Likelihood Ratio test can be performed in the context of multivariate VAR models.
In the present research article, the parameters of multivariate VAR model have been estimated by using the method of maximum likelihood estimation based on ordinary least squares regression. The dispersion matrix of errors in multivariate VAR model has been estimated by using Internally Studenti zed residuals. A test procedure has been developed is testing number of lags of variable for multivariate VAR model by using the Likelihood Ratio test.Pages: 442-450 | Views: 410 | Downloads: 7Download Full Article: Click Here
How to cite this article:
M Ramesh, P Vishnu Priya, P Srivyshnavi, G Madhusudan, K Aswini, P Balasiddamuni. Estimation of parameters of a multivariate time series var model. Int J Stat Appl Math 2018;3(1):442-450.