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Integration, Kointegration und die Langzeitprognose von Kreditausfallzyklen
[Integration, Cointegration and Long-Horizont Forecasting of Credit-Default-Cycles]

  • Wagatha, Matthias
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    Summary: This paper examines the longterm forecast performance of cointegrated systems relative to forecast performance of comparable VAR that fails to recognize that the system is characterized by cointegration. I use Monte Carlo simulation, real data sets, and multi-step-ahead forecasts to study this question. The cointegrated system I examine is composed of six vectors, five macoreconomic variables, and a credit-default-cycle. The forecasts produced by the vector error correction modell associated with this system are compared with those obtained from a corresponding differenced vector autoregression, as well as a vector autoregression based upon the levels of the data. Alternative measures of forecast accuracy (full-system) are discussed. My findings suggest that selective forecast performance improvement may be observed by incorporating knowledge of cointegration rank. Furthermore the results indicate that a cointegration modeling of credit risk should be favored against the prevalent level or differenced estimation.

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    File URL: http://mpra.ub.uni-muenchen.de/8602/1/MPRA_paper_8602.pdf
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    Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 8602.

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    Date of creation: 01 Jul 2007
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    Handle: RePEc:pra:mprapa:8602
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    15. Anderson, Richard G. & Hoffman, Dennis L. & Rasche, Robert H., 2002. "A vector error-correction forecasting model of the US economy," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 569-598, December.
    16. Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501, March.
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    18. Engle, Robert F. & Yoo, Byung Sam, 1987. "Forecasting and testing in co-integrated systems," Journal of Econometrics, Elsevier, vol. 35(1), pages 143-159, May.
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