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

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Author Info
Wagatha, Matthias
Abstract

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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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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Related research
Keywords: Integration Kointegration Langzeitprognose Kreditausfallzyklus Integration Cointegration Forecasting Credit-default-cycle

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Find related papers by JEL classification:
C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications

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  2. Thompson, Patrick A., 1990. "An MSE statistic for comparing forecast accuracy across series," International Journal of Forecasting, Elsevier, vol. 6(2), pages 219-227, July. [Downloadable!] (restricted)
  3. 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.
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  4. James MacKinnon, 1990. "Critical Values for Cointegration Tests," University of California at San Diego, Economics Working Paper Series 90-4, Department of Economics, UC San Diego. [Downloadable!]
  5. Osborn, Denise R. & Heravi, Saeed & Birchenhall, C. R., 1999. "Seasonal unit roots and forecasts of two-digit European industrial production," International Journal of Forecasting, Elsevier, vol. 15(1), pages 27-47, February. [Downloadable!] (restricted)
  6. 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. [Downloadable!] (restricted)
  7. Diebold, Francis X & Senhadji, Abdelhak S, 1996. "The Uncertain Unit Root in Real GNP: Comment," American Economic Review, American Economic Association, vol. 86(5), pages 1291-98, December. [Downloadable!] (restricted)
  8. Fildes, Robert, 1992. "The evaluation of extrapolative forecasting methods," International Journal of Forecasting, Elsevier, vol. 8(1), pages 81-98, June. [Downloadable!] (restricted)
  9. 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. [Downloadable!] (restricted)
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  10. Hoffman, Dennis L & Rasche, Robert H, 1996. "Assessing Forecast Performance in a Cointegrated System," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(5), pages 495-517, Sept.-Oct. [Downloadable!] (restricted)
  11. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254. [Downloadable!] (restricted)
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