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Generating random AR(p) and MA(q) Toeplitz correlation matrices

Chi Tim Ng and Harry Joe

Journal of Multivariate Analysis, 2010, vol. 101, issue 6, 1532-1545

Abstract: Methods are proposed for generating random (p+1)x(p+1) Toeplitz correlation matrices that are consistent with a causal AR(p) Gaussian time series model. The main idea is to first specify distributions for the partial autocorrelations that are algebraically independent and take values in (-1,1), and then map to the Toeplitz matrix. Similarly, starting with pseudo-partial autocorrelations, methods are proposed for generating (q+1)x(q+1) Toeplitz correlation matrices that are consistent with an invertible MA(q) Gaussian time series model. The density can be uniform or non-uniform over the space of autocorrelations up to lag p or q, or over the space of autoregressive or moving average coefficients, by making appropriate choices for the densities of the (pseudo)-partial autocorrelations. Important intermediate steps are the derivations of the Jacobians of the mappings between the (pseudo)-partial autocorrelations, autocorrelations and autoregressive/moving average coefficients. The random generating methods are useful for models with a structured Toeplitz matrix as a parameter.

Keywords: Autoregressive; process; Beta; distribution; Longitudinal; data; Moving; average; process (search for similar items in EconPapers)
Date: 2010
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