Inference for impulse response coefficients from multivariate fractionally integrated processes |
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| Authors: | Richard T. Baillie George Kapetanios Fotis Papailias |
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| Affiliation: | 1. Department of Economics, Michigan State University, USA and Rimini Center for Economic Analysis, Italy;2. School of Economics and Finance, Queen Mary, University of London, UK;3. School of Economics and Finance, Queen Mary, University of London, UK;4. Queen's University Management School, Queens University Belfast, UK and quantf research |
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| Abstract: | This article 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 article extends the univariate analysis recently provided by Baillie and Kapetanios (2013 Baillie, R. T., Kapetanios, G. (2013). Estimation and inference for impulse response functions form univariate strongly persistent processes. Econometrics Journal 16:373–399.[Crossref], [Web of Science ®] , [Google Scholar]), and uses a semiparametric, 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. |
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| Keywords: | Confidence intervals fractional integration impulse responses long memory processes VARs |
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