Weak Convergence to a Matrix Stochastic Integral with Stable Processes
This paper generalizes the univariate results of Chan and Tran (1989, <italic>Econometric Theory</italic> 5, 354–362) and Phillips (1990, <italic>Econometric Theory</italic> 6, 44–62) to multivariate time series. We develop the limit theory for the least-squares estimate of a VAR(l) for a random walk with independent and identically distributed errors and for I(1) processes with weakly dependent errors whose distributions are in the domain of attraction of a stable law. The limit laws are represented by functional of a stable process. A semiparametric correction is used in order to asymptotically eliminate the “bias” term in the limit law. These results are also an extension of the multivariate limit theory for square-integrable disturbances derived by Phillips and Durlauf (1986, <italic>Review of Economic Studies</italic> 53, 473–495). Potential applications include tests for multivariate unit roots and cointegration.
Year of publication: |
1997
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Authors: | Caner, Mehmet |
Published in: |
Econometric Theory. - Cambridge University Press. - Vol. 13.1997, 04, p. 506-528
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Publisher: |
Cambridge University Press |
Description of contents: | Abstract [journals.cambridge.org] |
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