This paper considers the issue of seasonal cointegrating rank selection by information criteria as the extension of Cheng and Phillips (The Econometrics Journal (2009), Vol. 12, pp. S83-S104). The method does not require the specification of lag length in vector autoregression, is convenient in empirical work, and is in a semiparametric context because it allows for a general short memory error component in the model with only lags related to error correction terms. Some limit properties of usual information criteria are given for the rank selection and small Monte Carlo simulations are conducted to evaluate the performances of the criteria. © Springer-Verlag Berlin Heidelberg 2010.
CITATION STYLE
Seong, B., Ahn, S. K., & Cho, S. (2010). Semiparametric seasonal cointegrating rank selection. In Proceedings of COMPSTAT 2010 - 19th International Conference on Computational Statistics, Keynote, Invited and Contributed Papers (pp. 297–304). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-7908-2604-3_27
Mendeley helps you to discover research relevant for your work.