HAC estimation and strong linearity testing in weak ARMA models

Christian Francq and Jean-Michel Zakoian

Journal of Multivariate Analysis, 2007, vol. 98, issue 1, 114-144

Abstract: In the framework of ARMA models, we consider testing the reliability of the standard asymptotic covariance matrix (ACM) of the least-squares estimator. The standard formula for this ACM is derived under the assumption that the errors are independent and identically distributed, and is in general invalid when the errors are only uncorrelated. The test statistic is based on the difference between a conventional estimator of the ACM of the least-squares estimator of the ARMA coefficients and its robust HAC-type version. The asymptotic distribution of the HAC estimator is established under the null hypothesis of independence, and under a large class of alternatives. The asymptotic distribution of the proposed statistic is shown to be a standard [chi]2 under the null, and a noncentral [chi]2 under the alternatives. The choice of the HAC estimator is discussed through asymptotic power comparisons. The finite sample properties of the test are analyzed via Monte Carlo simulation.

Keywords: ARMA; models; Nonlinear; models; Least-squares; estimator; Long-run; variance; matrix; Diagnostic; checking; Kernel; estimator (search for similar items in EconPapers)
Date: 2007
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Citations: View citations in EconPapers (15)

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