Estimating Variances and Covariances in a Non-stationary Multivariate Time Series Using the K-matrix

Authors: Stephen P. Smith

A second order time series model is described, and generalized to the multivariate situation. The model is highly flexible, and is suitable for non-parametric regression, coming with unequal time steps. The resulting K-matrix is described, leading to its possible factorization and differentiation using general purpose software that was recently developed. This makes it possible to estimate variance matrices in the multivariate model corresponding the signal and noise components of the model, by restricted maximum likelihood. A nested iteration algorithm is presented for conducting the maximization, and an illustration of the methods are demonstrated on a 4-variate time series with 89 observations.

Comments: 15 Pages.

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Submission history

[v1] 2019-01-06 15:23:31 (removed)
[v2] 2019-01-08 22:24:19

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