Evaluating exact VARMA likelihood and its gradient when data are incomplete

Author:

Jonasson Kristjan1,Ferrando Sebastian E.2

Affiliation:

1. University of Iceland, Reykjavik, Iceland

2. Ryerson University

Abstract

A detailed description of an algorithm for the evaluation and differentiation of the likelihood function for VARMA processes in the general case of missing values is presented. The method is based on combining the Cholesky decomposition method for complete data VARMA evaluation and the Sherman-Morrison-Woodbury formula. Potential saving for pure VAR processes is discussed and formulae for the estimation of missing values and shocks are provided. A theorem on the determinant of a low rank update is proved. Matlab implementation of the algorithm is in a companion article.

Publisher

Association for Computing Machinery (ACM)

Subject

Applied Mathematics,Software

Reference24 articles.

1. An algorithm for the exact likelihood of a mixed autoregressive-moving average process

2. Exact likelihood of vector autoregressive-moving average process with missing or aggregated data

3. Golub G. H. and Van Loan C. F. 1983. Matrix Computations. North Oxford Academic Oxford UK. Golub G. H. and Van Loan C. F. 1983. Matrix Computations. North Oxford Academic Oxford UK.

4. Maximum likelihood estimation of regression models with autoregressive-moving average disturbances;Harvey A. C.;Biometrika,1979

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