Integration, Kointegration und die Langzeitprognose von Kreditausfallzyklen
[Integration, Cointegration and Long-Horizont Forecasting of Credit-Default-Cycles]
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.
|Date of creation:||01 Jul 2007|
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- Karim Abadir & Kaddour Hadri & Elias Tzavalis, "undated".
"The Influence of VAR Dimensions on Estimator Biases,"
96/14, Department of Economics, University of York.
- Karim M. Abadir & Kaddour Hadri & Elias Tzavalis, 1999. "The Influence of VAR Dimensions on Estimator Biases," Econometrica, Econometric Society, vol. 67(1), pages 163-182, January.
- Armstrong, J. Scott & Collopy, Fred, 1992. "Error measures for generalizing about forecasting methods: Empirical comparisons," International Journal of Forecasting, Elsevier, vol. 8(1), pages 69-80, June.
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