Vector Autoregressive Modelling Of Fire Signals

Wavelet based time-varying vector autoregressive modelling

Vector autoregressive (VAR) modelling is one of the most popular approaches in multivariate time series analysis. The parameters interpretation is simple, and provide an intuitive identification of relationships and Granger causality among time series. However, the VAR modelling requires stationarity conditions which could not be valid in many practical applications. Locally stationary or time ...

Vector Autoregressive Model Selection: Gross Domestic Product and Europe Oil Prices Data Modelling

 We consider the problem of model selection in vector autoregressive model with Normal innovation. Tests such as Vuong's and Cox's tests are provided for order and model selection, i.e. for selecting the order and a suitable subset of regressors, in vector autoregressive model. We propose a test as a modified log-likelihood ratio test for selecting subsets of regressors. The Europe oil prices, ...

Modelling of cointegration in the vector autoregressive model

A survey is given of some results obtained for the cointegrated VAR. The Granger representation theorem is discussed and the notions of cointegration and common trends Ž . are defined. The statistical model for cointegrated I 1 variables is defined, and it is shown how hypotheses on the cointegrating relations can be estimated under suitable identification conditions. The asymptotic theory is b...

Recursive Subspace Model Identification Based On Vector Autoregressive Modelling

Recursive subspace model identification (RSMI) has been developed for a decade. Most of RSMIs are only applied for open loop data. In this paper, we propose a new recursive subspace model identification which can be applied under open loop and closed loop data. The key technique of this derivation of the proposed algorithm is to bring the Vector Auto Regressive with eXogenous input (VARX) model...

Vector Autoregressive Models