Print Email Facebook Twitter Vector Autoregressive Order Selection in Practice Title Vector Autoregressive Order Selection in Practice Author Broersen, P.M.T. Faculty Applied Sciences Department Multi-Scale Physics Date 2009-07-07 Abstract Vector time series analysis takes the same model order and model type for the different signals involved. Selection criteria have been developed to select the best order to simultaneously predict the different components of the vector. The prediction of single channels might require a different order or type for the best accuracy of each separate signal. That can become a problem in multichannel analysis if the individual signals have completely different model orders. Therefore, univariate and multichannel spectra are not similar. Furthermore, the selected order may vary in practice with the number of signals that are included in a vector. A turbulence example shows the results of order selection and the consequences in estimating the coherency between the same two components from vector signals with dimensions two and five. Subject autoregressive modelcoherence estimationmagnitude-squared coherence (MSC)order selectiontime series analysis To reference this document use: http://resolver.tudelft.nl/uuid:bc49f66e-5c4a-4833-b8d7-a5b96d66f5af DOI https://doi.org/10.1109/TIM.2009.2015631 Publisher IEEE ISSN 0018-9456 Source IEEE Transactions on Instrumentation and Measurement, 58 (8), 2009 Part of collection Institutional Repository Document type journal article Rights (c) 2009 IEEE Files PDF broersen2009e.pdf 1.18 MB Close viewer /islandora/object/uuid:bc49f66e-5c4a-4833-b8d7-a5b96d66f5af/datastream/OBJ/view