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Macroeconomic Interval Forecasting: The Case of Assessing the Risk of Deflation in Germany

  • Dora Borbély
  • Carsten-Patrick Meier
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    This paper proposes an approach for estimating the uncertainty associated with model-based macroeconomic forecasts. We argue that estimated forecast intervals should account for the uncertainty arising from selecting the specification of an empirical forecasting model from the sample data. To allow this uncertainty to be considered systematically, we formalize a model selection procedure that specifies the lag structure of a model and accounts for aberrant observations. The procedure can be used to bootstrap the complete model selection process when estimating forecast intervals. We apply the procedure to assess the risk of deflationary developments occurring in Germany over the next four years.

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    File URL: https://www.ifw-members.ifw-kiel.de/publications/macroeconomic-interval-forecasting-the-case-of-assessing-the-risk-of-deflation-in-germany/kap1153.pdf
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    Paper provided by Kiel Institute for the World Economy in its series Kiel Working Papers with number 1153.

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    Length: 17 pages
    Date of creation: Mar 2003
    Date of revision:
    Handle: RePEc:kie:kieliw:1153
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    1. Findley, David F, et al, 1998. "New Capabilities and Methods of the X-12-ARIMA Seasonal-Adjustment Program," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 127-52, April.
    2. Krasker, William S. & Kuh, Edwin & Welsch, Roy E., 1983. "Estimation for dirty data and flawed models," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 1, chapter 11, pages 651-698 Elsevier.
    3. Franses, Philip Hans & Lucas, Andre, 1998. "Outlier Detection in Cointegration Analysis," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(4), pages 459-68, October.
    4. Hodrick, Robert J & Prescott, Edward C, 1997. "Postwar U.S. Business Cycles: An Empirical Investigation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 29(1), pages 1-16, February.
    5. Jeremy Berkowitz & Lutz Kilian, 1996. "Recent developments in bootstrapping time series," Finance and Economics Discussion Series 96-45, Board of Governors of the Federal Reserve System (U.S.).
    6. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October.
    7. Kilian, Lutz, 2001. "Impulse Response Analysis in Vector Autoregressions with Unknown Lag Order," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(3), pages 161-79, April.
    8. Marianne Baxter & Robert G. King, 1999. "Measuring Business Cycles: Approximate Band-Pass Filters For Economic Time Series," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 575-593, November.
    9. Neil R. Ericsson, 2001. "Forecast uncertainty in economic modeling," International Finance Discussion Papers 697, Board of Governors of the Federal Reserve System (U.S.).
    10. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
    11. Clements, Michael P. & Taylor, Nick, 2001. "Bootstrapping prediction intervals for autoregressive models," International Journal of Forecasting, Elsevier, vol. 17(2), pages 247-267.
    12. Pascual, Lorenzo & Romo, Juan & Ruiz, Esther, 2001. "Effects of parameter estimation on prediction densities: a bootstrap approach," International Journal of Forecasting, Elsevier, vol. 17(1), pages 83-103.
    13. repec:cup:cbooks:9780521634809 is not listed on IDEAS
    14. Bruce E. Hansen, 1999. "Discussion of 'Data mining reconsidered'," Econometrics Journal, Royal Economic Society, vol. 2(2), pages 192-201.
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