Title
Identification Of Vector Autoregressive Models With Granger And Stability Constraints
Abstract
In this work, we introduce an iterative method for the estimation of vector autoregressive (VAR) models with Granger and stability constraints. When the order of the model (p) and the Granger sparsity pattern (GSP) are not known, the newly proposed method is integrated in a two-stage approach. An information theoretic (IT) criterion is used in the first stage for selecting the value of p. In the second stage, a set of possible candidates for GSP are produced by applying the Wald test, and the best one is chosen with an IT criterion. In experiments with synthetic data, we demonstrate that our method yields more accurate forecasts than the state-of-art algorithm that is based on convex optimization and fits models which are guaranteed to be stable.
Year
DOI
Venue
2019
10.23919/EUSIPCO.2019.8902516
2019 27TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO)
Keywords
Field
DocType
Vector autoregressive models, Granger causality, stability, convex optimization, information theoretic criteria
Autoregressive model,Mathematical optimization,Iterative method,Granger causality,Synthetic data,Wald test,Convex optimization,Stability constraints,Mathematics
Conference
ISSN
Citations 
PageRank 
2076-1465
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Bogdan Dumitrescu110722.76
Ciprian Doru Giurcaneanu24312.44
Yixia Ding300.34