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Linear least squares estimation of the first order moving average parameter

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Author Info
Emili Valdero Mora (Universitat de Barcelona)
Abstract

We propose an iterative procedure to minimize the sum of squares function which avoids the nonlinear nature of estimating the rst order moving average parameter and provides a closed form of the estimator. The asymptotic properties of the method are discussed and the consistency of the linear least squares estimator is proved for the invertible case. We perform various Monte Carlo experiments in order to compare the sample properties of the linear least squares estimator with its nonlinear counterpart for the conditional and unconditional cases. Some examples are also discussed.

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Paper provided by Universitat de Barcelona. Espai de Recerca en Economia in its series Working Papers in Economics with number 80.

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Length: 25 pages
Date of creation: 2002
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Handle: RePEc:bar:bedcje:200280

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Postal: Espai de Recerca en Economia, Facultat de Ciències Econòmiques. Tinent Coronel Valenzuela, Num 1-11 08034 Barcelona. Spain.
Web page: http://www.ere.ub.es
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Find related papers by JEL classification:
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions

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  1. Davidson, James E. H., 1981. "Problems with the estimation of moving average processes," Journal of Econometrics, Elsevier, vol. 16(3), pages 295-310, August. [Downloadable!] (restricted)
  2. Sargan, J D & Bhargava, Alok, 1983. "Maximum Likelihood Estimation of Regression Models with First Order Moving Average Errors When the Root Lies on the Unit Circle," Econometrica, Econometric Society, vol. 51(3), pages 799-820, May. [Downloadable!] (restricted)
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This page was last updated on 2009-11-11.

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