Skip to main content
  • 296 Accesses

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 54.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • LeSage, J. P. and M. Magura (1992). “A mixture-model approach to combining forecasts.” Journal of Business & Economic Statistics 10, 445–452.

    Google Scholar 

  • Li, D. T., and J. H. Dorfman (1995). “A robust approach to predicting fluctuations in state-level employment growth.” Journal of Regional Science 35, 471–484.

    Google Scholar 

  • Li, D. T., and J. H. Dorfman (1996). “Predicting turning points through the integration of multiple models.” Journal of Business & Economic Statistics 14, 421–428.

    CAS  Google Scholar 

  • Min, C. K., and A. Zellner (1993). “Bayesian and non-Bayesian methods for combining models and forecasts with applications to forecasting international growth rates.” Journal of Econometrics 56, 89–118.

    Article  Google Scholar 

  • Monahan, J. F. (1983). “Fully Bayesian analysis of ARMA time series models.” Journal of Econometrics 21, 307–331.

    Article  MATH  Google Scholar 

  • Pole, A., M. West, and J. Harrison (1994). Applied Bayesian Forecasting and Time Series Analysis. New York: Chapman-Hall.

    Google Scholar 

  • Pratt, J. W., H. Raiffa, and R. Schlaifer (1965). Introduction to Statistical Decision Theory, New York: McGraw-Hill Book Company.

    Google Scholar 

  • Thompson, P. A., and R. B. Miller (1986). “Sampling the future: A Bayesian approach to forecasting from univariate time series models.” Journal of Business & Economic Statistics 4, 427–436.

    Google Scholar 

  • West, M., and J. Harrison (1989). Bayesian Forecasting and Dynamic Models. New York: Springer-Verlag.

    Google Scholar 

  • Zellner, A., and C. Hong (1988). “Bayesian methods for forecasting turning points in economic time series: Sensitivity of forecasts to asymmetry of loss structures” Leading Economic Indicators: New Approaches and Forecasting Records, K. Lahiri and G. Moore, Eds., Cambridge: Cambridge University Press.

    Google Scholar 

  • Zellner, A., and C. Hong (1989). “Forecasting international growth rates using Bayesian shrinkage and other procedures.” Journal of Econometrics 40, 183–202.

    Article  Google Scholar 

  • Zellner, A., C. Hong, and G. M. Gulati (1990). “Turning points in economic time series, loss structures and Bayesian forecasting,” Bayesian and Likelihood Methods in Statistics and Econometrics: Essays in Honor of George A. Barnard, S. Geisser, J. Hodges, S. J. Press, and A. Zellner, Eds., Amsterdam: North Holland, pp. 371–393.

    Google Scholar 

  • Zellner, A., C. Hong, and C. Min (1991). “Forecasting turning points in international output growth rates using Bayesian exponentially weighted autoregression, time-varying parameter, and pooling techniques,” Journal of Econometrics 49, 275–304.

    Article  Google Scholar 

  • Zellner, A., and P. E. Rossi (1984). “Bayesian analysis of dichotomous quantal response models.” Journal of Econometrics 25, 365–393.

    Article  MathSciNet  Google Scholar 

Download references

Rights and permissions

Reprints and permissions

Copyright information

© 1997 Springer-Verlag New York, Inc.

About this chapter

Cite this chapter

(1997). Forecasting. In: Bayesian Economics Through Numerical Methods. Springer, New York, NY. https://doi.org/10.1007/0-387-22635-4_8

Download citation

  • DOI: https://doi.org/10.1007/0-387-22635-4_8

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-98233-5

  • Online ISBN: 978-0-387-22635-4

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics