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A Comparison of Estimation Methods for Vector Autoregressive Moving-Average Models

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Christian Kascha

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Abstract

Classical Gaussian maximum likelihood estimation of mixed vector autoregressive moving-average models is plagued with various numerical problems and has been considered di±cult 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.

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Paper provided by European University Institute in its series Economics Working Papers with number ECO2007/12.

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Date of creation: 2007
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Handle: RePEc:eui:euiwps:eco2007/12

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Related research
Keywords: VARMA Models; Estimation Algorithms; Forecasting;

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Find related papers by JEL classification:
C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions
C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Statistical Simulation Methods
C63 - Mathematical and Quantitative Methods - - Mathematical Methods and Programming - - - Computational Techniques

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Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Jesus Fernandez-Villaverde & Juan Rubio-Ramirez & Thomas J. Sargent, 2005. "A, B, C's (and D)'s for Understanding VARs," NBER Technical Working Papers 0308, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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  2. Kapetanios, George, 2003. "A note on an iterative least-squares estimation method for ARMA and VARMA models," Economics Letters, Elsevier, vol. 79(3), pages 305-312, June. [Downloadable!] (restricted)
  3. George Kapetanios, 2002. "A Note on an Iterative Least Squares Estimation Method for ARMA and VARMA Models," Working Papers 467, Queen Mary, University of London, Department of Economics. [Downloadable!]
  4. Dietmar Bauer, 2005. "Comparing the CCA Subspace Method to Pseudo Maximum Likelihood Methods in the case of No Exogenous Inputs," Journal of Time Series Analysis, Blackwell Publishing, vol. 26(5), pages 631-668, 09. [Downloadable!] (restricted)
  5. Bauer, Dietmar, 2005. "Estimating Linear Dynamical Systems Using Subspace Methods," Econometric Theory, Cambridge University Press, vol. 21(01), pages 181-211, February. [Downloadable!]
  6. L. Kavalieris & E. J. Hannan & M. Salau, 2003. "Generalized Least Squares Estimation Of Arma Models," Journal of Time Series Analysis, Blackwell Publishing, vol. 24(2), pages 165-172, 03. [Downloadable!] (restricted)
  7. Cooley, Thomas F. & Dwyer, Mark, 1998. "Business cycle analysis without much theory A look at structural VARs," Journal of Econometrics, Elsevier, vol. 83(1-2), pages 57-88. [Downloadable!] (restricted)
  8. Lutkepohl, Helmut & Poskitt, D S, 1996. "Specification of Echelon-Form VARMA Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(1), pages 69-79, January.
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Cited by:
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  1. Boubacar Mainassara, Yacouba & Francq, Christian, 2009. "Estimating structural VARMA models with uncorrelated but non-independent error terms," MPRA Paper 15141, University Library of Munich, Germany. [Downloadable!]
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