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A Note on an Iterative Least Squares Estimation Method for ARMA and VARMA Models

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Author Info
George Kapetanios () (Queen Mary, University of London)

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Abstract

In this note we suggest a new iterative least squares method for estimating scalar and vector ARMA models. A Monte Carlo study shows that the method has better small sample properties than existing least squares methods and compares favourably with maximum likelihood estimation as well.

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Publisher Info
Paper provided by Queen Mary, University of London, Department of Economics in its series Working Papers with number 467.

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Date of creation: Nov 2002
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Handle: RePEc:qmw:qmwecw:wp467

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Related research
Keywords: ARMA models;

Find related papers by JEL classification:
C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions

This paper has been announced in the following NEP Reports:

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  1. Helmut Luetkepohl, 2004. "Forecasting with VARMA Models," Economics Working Papers ECO2004/25, European University Institute. [Downloadable!]
    Other versions:
  2. Christian Kascha, 2007. "A Comparison of Estimation Methods for Vector Autoregressive Moving-Average Models," Economics Working Papers ECO2007/12, European University Institute. [Downloadable!]
  3. Christian Kascha & Karel Mertens, 2006. "Business Cycle Analysis and VARMA models," Economics Working Papers ECO2006/37, European University Institute. [Downloadable!]
    Other versions:
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This page was last updated on 2009-11-7.

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