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On Mixture Periodic Vector Autoregressive Models
Communications in Statistics - 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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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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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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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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On a mixture vector autoregressive model
Canadian Journal of Statistics, 2007AbstractThe authors show how to extend univariate mixture autoregressive models to a multivariate time series context. Similar to the univariate case, the multivariate model consists of a mixture of stationary or nonstationary autoregressive components.
Wong, CS, Yau, CW, Fong, PW, Li, WK
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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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Statistical Analysis Of Mixture Vector Autoregressive Models
Scandinavian Journal of Statistics, 2016AbstractIn 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 ...
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Linkage vector autoregressive model
Applied Stochastic Models in Business and IndustryAbstractTo accommodate linkage effects of individuals, we develop a new linkage vector autoregressive (LAR) model for dynamic panel data. A main feature of the LAR model is incorporating dynamic network information in autoregressive time series modeling. The dynamic network can be given, or we can formulate the network links as a function of historical
Manabu Asai, Mike K. P. So
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Graphical models for structural vector autoregressions [PDF]
, 2003 The identification of a VAR requires differentiating between correlation and causation. This paper presents a method to deal with this problem. Graphical models, which provide a rigorous language to analyze the statistical and logical properties of causal relations, associate a particular set of vanishing partial correlations to every possible causal ...
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GMM Estimation of Non-Gaussian Structural Vector Autoregression
Journal of Business and Economic Statistics, 2021Markku Lanne, Jani Luoto
exaly