Date: 2007
Type: Working Paper
A Comparison of Estimation Methods for Vector Autoregressive Moving-Average Models
Working Paper, EUI ECO, 2007/12
KASCHA, Christian, A Comparison of Estimation Methods for Vector Autoregressive Moving-Average Models, EUI ECO, 2007/12 - https://hdl.handle.net/1814/6921
Retrieved from Cadmus, EUI Research Repository
Classical Gaussian maximum likelihood estimation of mixed vector autoregressive moving-average models is plagued with various numerical problems and has been considered difficult by many applied researchers. These disadvantages could have led to the dominant use of vector autoregressive models in macroeconomic research. Therefore, several other, simpler estimation methods have been proposed in the literature. In this paper these methods are compared by means of a Monte Carlo study. Different evaluation criteria are used to judge the relative performances of the algorithms.
Cadmus permanent link: https://hdl.handle.net/1814/6921
ISSN: 1725-6704
Series/Number: EUI ECO; 2007/12
Publisher: European University Institute
Keyword(s): C32 C15 C63 VARMA models Estimation algorithms Forecasting