Comovements in Large Systems
AbstractIn this paper we study various methods for detecting the co integrating rank as the number of variables gets large. We show that the use of standard tools will always lead to misleading inferences in such settings due to excessive size distortions. Particularly the LR test tends to produce too much cointegration. We introduce a new test statistic that displays excellent size properties in both small and large systems. In addition we propose a model selection procedure for selecting the cointegrating rank. A new criterion outperforms the standard information-theoretic criteria (AIC, BIC).
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Bibliographic InfoPaper provided by Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) in its series CORE Discussion Papers with number 1994065.
Date of creation: 01 Nov 1994
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cointegration; information criteria; large systems; likelihood ratio tests;
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- Marie-Josée Godbout & Simon van Norden, 1997. "Reconsidering Cointegration in International Finance: Three Case Studies of Size Distortion in Finite Samples," Working Papers 97-1, Bank of Canada.
- Lee, Tae-Hwy & Tse, Yiuman, 1996. "Cointegration tests with conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 73(2), pages 401-410, August.
- Godbout, M.J. & Van Norden, S., 1996. "Unit-Root Test and Excess Returns," Working Papers 96-10, Bank of Canada.
- Franses, Philip Hans & Kloek, Teun & Lucas, Andre, 1998. "Outlier robust analysis of long-run marketing effects for weekly scanning data," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 293-315, November.
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