Efficient inference in multivariate fractionally integrated time series models
AbstractWe consider statistical inference for multivariate fractionally integrated time series models using a computationally simple conditional likelihood procedure which has recently been shown to be efficient in the univariate case. We show that those results generalize to the present multivariate setup, e.g. allowing us to efficiently estimate the memory parameters of vector ARFIMA models or test if two or more series are integrated of the same possibly fractional order. In particular, we show that all the desirable properties from standard statistical analysis apply for the time domain maximum likelihood estimator and related test statistics, i.e. consistency, standard asymptotic distributional properties, and under Gaussianity asymptotic efficiency. The finite sample properties of the likelihood ratio test are evaluated by Monte Carlo experiments, which show that rejection frequencies are very close to the asymptotic local power for samples as small as n=100.
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Bibliographic InfoArticle provided by Royal Economic Society in its journal The Econometrics Journal.
Volume (Year): 7 (2004)
Issue (Month): 1 (06)
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Other versions of this item:
- Morten Oerregaard Nielsen, . "Efficient Inference in Multivariate Fractionally Integrated Time Series Models," Economics Working Papers 2002-6, School of Economics and Management, University of Aarhus.
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
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