Baxters inequality for finite predictor coefficients of multivariate long-memory stationary processes | Academic Article individual record
abstract

2018 ISI/BS. For a multivariate stationary process, we develop explicit representations for the finite predictor coefficient matrices, the finite prediction error covariance matrices and the partial autocorrelation function (PACF) in terms of the Fourier coefficients of its phase function in the spectral domain. The derivation is based on a novel alternating projection technique and the use of the forward and backward innovations corresponding to predictions based on the infinite past and future, respectively.We show that such representations are ideal for studying the rates of convergence of the finite predictor coefficients, prediction error covariances, and the PACF as well as for proving a multivariate version of Baxter's inequality for a multivariate FARIMA process with a common fractional differencing order for all components of the process.

publication outlet

Bernoulli

author list (cited authors)
Inoue, A., Kasahara, Y., & Pourahmadi, M.
publication date
2018
keywords
  • Predictor Coefficients
  • Partial Autocorrelation Functions
  • Phase Functions
  • Multivariate Stationary Processes
  • Baxter's Inequality
  • Long Memory
citation count

5

identifier
330173SE
Digital Object Identifier (DOI)
start page
1202
end page
1232
volume
24
issue
2