Asymptotic inference results for multivariate long-memory processes
AbstractIn this paper, we extend the well-known Sims, Stock and Watson (SSW) (Sims et al. 1990; Econometrica 56, 113-44), analysis on estimation and testing in vector autoregressive process (VARs) with integer unit roots and deterministic components to a more general set-up where non-stationary fractionally integrated (NFI) processes are considered. In particular, we focus on partial VAR models where the conditioning variables are NFI since this is the only finite-lag VAR model compatible with such processes. We show how SSW's conclusions remain valid. This means that whenever a block of coefficients in the partial VAR can be written as coefficients on zero-mean I(0) regressors in models including a constant term, they will have a joint asymptotic normal distribution. Monte Carlo simulations and an empirical application of our theoretical results are also provided. Copyright Royal Economic Socciety 2004
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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)
Other versions of this item:
- Dolado, Juan José & Mármol, Francesc, . "Asymptotic inference results for multivariate long-memory processes," Open Access publications from Universidad Carlos III de Madrid info:hdl:10016/3207, Universidad Carlos III de Madrid.
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