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Calibration-Based Predictive Distributions: An Application of Prequential Analysis to Interest Rates, Money, Prices, and Output

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  • Kling, John L
  • Bessler, David A

Abstract

Some techniques of probability forecasting are applied to time-series data on interest rates, money stock, consumer prices, and output. A sequential method for debiasing (recalibrating) predictive distributions and outcomes is developed, and the authors estimated sequences of unadjusted and recalibrated distributions are tested for calibration. After recalibration, the calibration hypothesis cannot be rejected for most of the time-series and forecast horizons. Furthermore, traditional point forecasts can be improved when the forecasts are derived from recalibrated distributions. Copyright 1989 by the University of Chicago.

Suggested Citation

  • Kling, John L & Bessler, David A, 1989. "Calibration-Based Predictive Distributions: An Application of Prequential Analysis to Interest Rates, Money, Prices, and Output," The Journal of Business, University of Chicago Press, vol. 62(4), pages 477-499, October.
    • Handle: RePEc:ucp:jnlbus:v:62:y:1989:i:4:p:477-99
    DOI: 10.1086/296474
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    Cited by:

    1. Goodwin, Barry K., 1992. "Forecasting Cattle Prices in the Presence of Structural Change," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 24(2), pages 11-22, December.
    2. David A. Bessler & Shahriar Kibriya & Junyi Chen & Edwin Price, 2016. "On Forecasting Conflict in the Sudan: 2009–2012," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(2), pages 179-188, March.
    3. Frank Schorfheide & Dongho Song, 2015. "Real-Time Forecasting With a Mixed-Frequency VAR," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(3), pages 366-380, July.
    4. David Bessler & Robert Ruffley, 2004. "Prequential analysis of stock market returns," Applied Economics, Taylor & Francis Journals, vol. 36(5), pages 399-412.
    5. Huang, Wei & Lai, Pei-Chun & Bessler, David A., 2018. "On the changing structure among Chinese equity markets: Hong Kong, Shanghai, and Shenzhen," European Journal of Operational Research, Elsevier, vol. 264(3), pages 1020-1032.
    6. Andres Trujillo-Barrera & Philip Garcia & Mindy L Mallory, 2018. "Short-term price density forecasts in the lean hog futures market," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 45(1), pages 121-142.
    7. Federico Bassetti & Roberto Casarin & Francesco Ravazzolo, 2018. "Bayesian Nonparametric Calibration and Combination of Predictive Distributions," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(522), pages 675-685, April.
    8. Shackleton, Mark B. & Taylor, Stephen J. & Yu, Peng, 2010. "A multi-horizon comparison of density forecasts for the S&P 500 using index returns and option prices," Journal of Banking & Finance, Elsevier, vol. 34(11), pages 2678-2693, November.
    9. Cardani, Roberta & Paccagnini, Alessia & Villa, Stefania, 2019. "Forecasting with instabilities: An application to DSGE models with financial frictions," Journal of Macroeconomics, Elsevier, vol. 61(C), pages 1-1.
    10. Michael K. Adjemian & Valentina G. Bruno & Michel A. Robe, 2020. "Incorporating Uncertainty into USDA Commodity Price Forecasts," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(2), pages 696-712, March.
    11. Kyle E. Binder & Mohsen Pourahmadi & James W. Mjelde, 2020. "The role of temporal dependence in factor selection and forecasting oil prices," Empirical Economics, Springer, vol. 58(3), pages 1185-1223, March.
    12. Duangnate, Kannika & Mjelde, James W., 2017. "Comparison of data-rich and small-scale data time series models generating probabilistic forecasts: An application to U.S. natural gas gross withdrawals," Energy Economics, Elsevier, vol. 65(C), pages 411-423.
    13. Herbst, Edward & Schorfheide, Frank, 2012. "Evaluating DSGE model forecasts of comovements," Journal of Econometrics, Elsevier, vol. 171(2), pages 152-166.
    14. Clements, Michael P., 2018. "Are macroeconomic density forecasts informative?," International Journal of Forecasting, Elsevier, vol. 34(2), pages 181-198.
    15. Casillas-Olvera, Gabriel & Bessler, David A., 2006. "Probability forecasting and central bank accountability," Journal of Policy Modeling, Elsevier, vol. 28(2), pages 223-234, February.
    16. Negro, Marco Del & Schorfheide, Frank, 2013. "DSGE Model-Based Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 57-140, Elsevier.
    17. Michael P. Clements & Nick Taylor, 2003. "Evaluating interval forecasts of high-frequency financial data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(4), pages 445-456.
    18. Anthony Tay & Kenneth F. Wallis, 2000. "Density Forecasting: A Survey," Econometric Society World Congress 2000 Contributed Papers 0370, Econometric Society.
    19. Patrick T. Brandt & John R. Freeman & Philip A. Schrodt, 2011. "Real Time, Time Series Forecasting of Inter- and Intra-State Political Conflict," Conflict Management and Peace Science, Peace Science Society (International), vol. 28(1), pages 41-64, February.
    20. Brian D. Deaton, 2018. "Effects of the Swiss Franc/Euro Exchange Rate Floor on the Calibration of Probability Forecasts," Forecasting, MDPI, vol. 1(1), pages 1-23, May.
    21. Dharmasena, Senarath & Bessler, David & Capps, Oral. Jr, 2016. "On the Evaluation of Probability Forecasts: An Application to Qualitative Choice Models," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 235424, Agricultural and Applied Economics Association.
    22. An-Sing Chen & Yan-Zhen Liu, 2008. "Enhancing hedging performance with the spanning polynomial projection," Quantitative Finance, Taylor & Francis Journals, vol. 8(6), pages 605-617.
    23. Roberta Cardani & Alessia Paccagnini & Stefania Villa, 2015. "Forecasting in a DSGE Model with Banking Intermediation: Evidence from the US," Working Papers 292, University of Milano-Bicocca, Department of Economics, revised Feb 2015.

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