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A Comparison of Estimation Methods for Vector AutoregressiveMoving-Average Models Christian Kascha Norges Bank, University of ZurichDecember 23, 2010 AbstractRecently, there has been a renewed interest in modeling economic time series by vectorautoregressive moving-average models. However, this class of models has been unpopularin practice because of Estimation problems and the complexity of the identification disadvantages could have led to the dominant use of Vector autoregressive modelsin macroeconomic research. In this paper, several simple Estimation Methods for vectorautoregressive moving-average models are compared among each other and with purevector autoregressive modeling using ordinary least squares by means of a Monte Carlostudy. Different evaluation criteria are used to judge the relative performances of classi cation: C32, C15, C63 Keywords: VARMA Models, Estimation Algorithms, Forecasting I thank Helmut L utkepohl and Anindya Banerjee for helpful comments and discussion.
A Comparison of Estimation Methods for Vector Autoregressive Moving-Average Models∗ Christian Kascha† Norges Bank, University of Zurich December 23, 2010
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Estimation and Comparison, High-Resolution Representation Learning for Human, Estimation, 3-D rigid body transformations: a comparison, Test Effort Estimation, Direction of Arrival Estimation, Direction of arrival, Stacked Hourglass Networks for Human Pose Estimation, Chapter 8 Weight Estimation, Review of the Basic Methodology, Power