Multiperiod-Ahead Predictive Densities and Model Comparison in Dynamic Models

  • Min Chung-ki

Abstract

This study proposes a new model comparison method which uses multiperiod-ahead predictive densities. Use of predictive densities allows us to incorporate the uncertainties associated with point forecasts in comparing the forecasting performances of various models. In the special case where one-period-ahead predictive densities are used, the method is equivalent to the Bayesian posterior odds. This new model-comparison method is contrasted to other measures of forecasting performance, such as the mean squared error, which don’t consider the uncertainties associated with point forecasts. To evaluate the multiperiod-ahead predictive densities in dynamic models, this study uses simulation methods.

Keywords

Marketing 

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References

  1. Bass, F. M. (1969), ‘A New Product Growth Model for Consumer Durables,’ Management Science, 15, 215–227CrossRefMATHGoogle Scholar
  2. Belsley, D. (1988), “Modelling and Forecasting Reliability,” International Journal of Forecasting, 4, 427–447CrossRefGoogle Scholar
  3. Berger, JO, and LR Pericchi (1992), “The Intrinsic Bayes Factor,” Purdue University Department of Statistical ReportGoogle Scholar
  4. Chow, GC (1973), Multiperiod Predictions from Stochastic Difference Equations by Bayesian Methods,Econometrica, 41, 109–118.Google Scholar
  5. Friedman, M, and A Schwartz (1991), Alternative Approaches to Analyzing Economic Data,American Economic Review81, 39–49Google Scholar
  6. Geisser, S . (1975), “The Predictive Sample Reuse Method with Application,” Journal of the American Statistical Association, 70, 320–328CrossRefMATHGoogle Scholar
  7. Gelfand, AE, DK Dey, and J Chang (1992), “Model Determination using Predictive Distributions with Implementation via Sampling-Based Methods,” Bayesian Statistics4, JM Bernardo, JO Berger, AP Dawid, and AFM Smith, eds., Oxford: Clarendon Press, 147–167Google Scholar
  8. Geweke, J (1994), “Bayesian Comparison of Econometric Models,” Manuscript.Google Scholar
  9. Min, C (1995), “Forecasting the Adoptions of New Consumer Durable Products,” Manuscript.Google Scholar
  10. Min, C, 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, 89–118CrossRefMATHGoogle Scholar
  11. 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, 427–436Google Scholar
  12. Zellner, A. (1986), “Biased Predictors, Rationality and the Evaluation of Forecasts,” Economics Letters, 21, 45–48CrossRefMathSciNetGoogle Scholar
  13. Zellner, A. (1994), “Time Series Analysis, Forecasting and Econometric Modeling: The Structural Econometric Modeling, Time Series Analysis (SEMTSA) Approach,” Journal of Forecasting, 13, 215–233CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 1996

Authors and Affiliations

  • Min Chung-ki
    • 1
  1. 1.George Mason UniversityUSA

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