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Statistical Analysis Of Mixture Vector Autoregressive Models
Scandinavian Journal of Statistics, 2016 AbstractIn this paper, we reconsider the mixture vector autoregressive model, which was proposed in the literature for modelling non‐linear time series. We complete and extend the stationarity conditions, derive a matrix formula in closed form for the autocovariance function of the process and prove a result on stable vector autoregressive moving ...
Maddalena Cavicchioli
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On Mixture Periodic Vector Autoregressive Models
Communications in Statistics Part B: Simulation and Computation, 2014This article deals with the study of some properties of a mixture periodically correlated n-variate vector autoregressive (MPVAR) time series model, which extends the mixture time invariant parameter n-vector autoregressive (MVAR) model that has been recently studied by Fong et al. (2007). Our main contributions here are, on the one side, the obtaining
Mohamed Bentarzi, L. Djeddou
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Order selection for vector autoregressive models
IEEE Transactions on Signal Processing, 2003Order-selection criteria for vector autoregressive (AR) modeling are discussed. The performance of an order-selection criterion is optimal if the model of the selected order is the most accurate model in the considered set of estimated models: here vector AR models. Suboptimal performance can be a result of underfit or overfit.
S de Waele, P M T Broersen
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Modelling of cointegration in the vector autoregressive model
Economic Modelling, 2000Abstract 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 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 ...
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2017 3rd International Conference on Big Data Computing and Communications (BIGCOM), 2017
VAR (Vector Auto-regressive) model is a kind of commonly used econometric-model. It is used to estimate the dynamic relationship of the endogenous variables without any prior constraints. Since VAR is one of the most easily operated models to deal with the analysis and prediction of multiple related economic indicators, more and more attention has been
Tao Li, Xueyu Li, Xu Zhang
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VAR (Vector Auto-regressive) model is a kind of commonly used econometric-model. It is used to estimate the dynamic relationship of the endogenous variables without any prior constraints. Since VAR is one of the most easily operated models to deal with the analysis and prediction of multiple related economic indicators, more and more attention has been
Tao Li, Xueyu Li, Xu Zhang
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Process Control for the Vector Autoregressive Model
Quality and Reliability Engineering International, 2012Multivariate monitoring techniques for serially correlated observations have been widely used in various applications. This study examines several issues that have arisen in relation to the statistical quality control for the vector autoregressive (VAR) model, using a Monte Carlo approach.
Cheng, T.-C., Hsieh, P.-H., Yang, S.-F.
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The Vector Autoregressive Model
1995Abstract Deals with the classical statistical analysis of the unrestricted vector autoregressive model. We give a necessary and sufficient condition for stationarity and a representation for the stationary solution. We derive the ordinary least squares estimators as maximum likelihood estimator and find the asymptotic properties of the ...
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THE COINTEGRATION PROPERTIES OF VECTOR AUTOREGRESSION MODELS
Journal of Time Series Analysis, 1991A stochastic sequence (scalar or vector) is integrated of order d (denoted I(d)) if it is purely non-deterministic and its d th difference is representable as a stationary invertible zero-mean ARMA process. A vector I(d) sequence \(\underset{\tilde{}} x_ t\) is cointegrated of degree b (CI(d,b)) if some of its elements are \(I(d-b+i)\), while for ...
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Sparse vector autoregressive models
2023The aim of this project is to give an overview of the literature on shrinkage techniques regarding vector autoregressive (VAR) models, with focus on lasso based methods. The techniques discussed, include the approach proposed by Hsu et al. (2008), which is based on the lasso method proposed by Tibshirani (1996), the approach proposed by Wilms (2016 ...
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On a constrained mixture vector autoregressive model
Mathematics and Computers in Simulation, 2013zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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