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Inference for Impulse Response Coefficients From Multivariate Fractionally Integrated Processes

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  • Richard T. Baillie

    (Department of Economics, Michigan State University, USA; School of Economics and Finance, Queen Mary University of London, UK; The Rimini Centre for Economic Analysis, Italy)

  • George Kapetanios

    (School of Economics and Finance, Queen Mary University of London, UK)

  • Fotis Papailias

    (Queen's University Management School, Queen's University Belfast, UK; quantf research, www.quantf.com)

Abstract

This paper considers a multivariate system of fractionally integrated time series and investigates the most appropriate way for estimating Impulse Response (IR) coefficients and their associated confidence intervals. The paper extends the univariate analysis recently provided by Baillie and Kapetanios (2013), and uses a semi parametric, time domain estimator, based on a vector autoregression (VAR) approximation. Results are also derived for the orthogonalized estimated IRs which are generally more practically relevant. Simulation evidence strongly indicates the desirability of applying the Kilian small sample bias correction, which is found to improve the coverage accuracy of confidence intervals for IRs. The most appropriate order of the VAR turns out to be relevant for the lag length of the IR being estimated.

Suggested Citation

  • Richard T. Baillie & George Kapetanios & Fotis Papailias, 2015. "Inference for Impulse Response Coefficients From Multivariate Fractionally Integrated Processes," Working Paper series 15-46, Rimini Centre for Economic Analysis.
    • Handle: RePEc:rim:rimwps:15-46
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