Evaluating exact VARMA likelihood and its gradient when data are incomplete

Kristjan Jonasson*, Sebastian E. Ferrando

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


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.

Original languageEnglish
JournalACM Transactions on Mathematical Software
Issue number1
Publication statusPublished - 1 Jul 2008

Other keywords

  • ARMA
  • Determinant of low rank update
  • Exact likelihood function
  • Incomplete data
  • Matrix derivative
  • Matrix differentiation
  • Missing values
  • Vector autoregressive moving average model


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