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Density forecasting through disaggregation

  • Kim, Kun Ho
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    In this paper, the revised expectations model (REM) is developed to incorporate economic agents' price expectation formation effects. With this incorporation, two models, an aggregate one sector model and a disaggregated multi-sector model, are estimated and used in density forecasting of the US real GDP growth rate. The experiment shows that use of the disaggregated version of the model, which incorporates price expectation effects along with modern Bayesian MCMC estimation and prediction techniques, produces more precise density forecasts than those yielded by either an aggregate version or benchmark forecasting models.

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    File URL: http://www.sciencedirect.com/science/article/B6V92-50KBP51-2/2/da9bc58e33ebddc024525e80317e6f48
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    Article provided by Elsevier in its journal International Journal of Forecasting.

    Volume (Year): 27 (2011)
    Issue (Month): 2 (April)
    Pages: 394-412

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    Handle: RePEc:eee:intfor:v:27:y::i:2:p:394-412
    Contact details of provider: Web page: http://www.elsevier.com/locate/ijforecast

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    1. Milton Friedman, 1957. "A Theory of the Consumption Function," NBER Books, National Bureau of Economic Research, Inc, number frie57-1, June.
    2. Engle, Robert F & Granger, Clive W J, 1987. "Co-integration and Error Correction: Representation, Estimation, and Testing," Econometrica, Econometric Society, vol. 55(2), pages 251-76, March.
    3. Taylor, John B, 1980. "Aggregate Dynamics and Staggered Contracts," Journal of Political Economy, University of Chicago Press, vol. 88(1), pages 1-23, February.
    4. Smets, Frank & Wouters, Rafael, 2004. "Forecasting with a Bayesian DSGE Model: An Application to the Euro Area," CEPR Discussion Papers 4749, C.E.P.R. Discussion Papers.
    5. Del Negro, Marco & Schorfheide, Frank & Smets, Frank & Wouters, Rafael, 2007. "On the Fit of New Keynesian Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 123-143, April.
    6. Milton Friedman, 1957. "Introduction to "A Theory of the Consumption Function"," NBER Chapters, in: A Theory of the Consumption Function, pages 1-6 National Bureau of Economic Research, Inc.
    7. A. Espasa & E. Senra & R. Albacete, 2002. "Forecasting inflation in the European Monetary Union: A disaggregated approach by countries and by sectors," The European Journal of Finance, Taylor & Francis Journals, vol. 8(4), pages 402-421.
    8. Frank Smets & Raf Wouters, 2002. "An estimated dynamic stochastic general equilibrium model of the euro area," Working Paper Research 35, National Bank of Belgium.
    9. Lars Peter Hansen & Thomas J. Sargent, 1979. "Formulating and estimating dynamic linear rational expectations models," Working Papers 127, Federal Reserve Bank of Minneapolis.
    10. Zellner, Arnold & Israilevich, Guillermo, 2005. "Marshallian Macroeconomic Model: A Progress Report," Macroeconomic Dynamics, Cambridge University Press, vol. 9(02), pages 220-243, April.
    11. Zellner, Arnold & Tobias, Justin, 2004. "A Note on Aggregation, Disaggregation and Forecasting Performance," Staff General Research Papers 12371, Iowa State University, Department of Economics.
    12. Taylor, John B, 1979. "Estimation and Control of a Macroeconomic Model with Rational Expectations," Econometrica, Econometric Society, vol. 47(5), pages 1267-86, September.
    13. Min, Chung-ki & Zellner, Arnold, 1993. "Bayesian and non-Bayesian methods for combining models and forecasts with applications to forecasting international growth rates," Journal of Econometrics, Elsevier, vol. 56(1-2), pages 89-118, March.
    14. Litterman, Robert B, 1986. "Forecasting with Bayesian Vector Autoregressions-Five Years of Experience," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 25-38, January.
    15. Sims, Christopher A, 1972. "Money, Income, and Causality," American Economic Review, American Economic Association, vol. 62(4), pages 540-52, September.
    16. Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-62, November.
    17. Granger, C. W. J., 1981. "Some properties of time series data and their use in econometric model specification," Journal of Econometrics, Elsevier, vol. 16(1), pages 121-130, May.
    18. DeJong, David N. & Ingram, Beth F. & Whiteman, Charles H., 2000. "A Bayesian approach to dynamic macroeconomics," Journal of Econometrics, Elsevier, vol. 98(2), pages 203-223, October.
    19. Chib, Siddhartha & Greenberg, Edward, 1995. "Hierarchical analysis of SUR models with extensions to correlated serial errors and time-varying parameter models," Journal of Econometrics, Elsevier, vol. 68(2), pages 339-360, August.
    20. Garcia-Ferrer, Antonio, et al, 1987. "Macroeconomic Forecasting Using Pooled International Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 5(1), pages 53-67, January.
    21. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    22. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-38, July.
    23. Arthur F. Burns & Wesley C. Mitchell, 1946. "Measuring Business Cycles," NBER Books, National Bureau of Economic Research, Inc, number burn46-1, June.
    24. Grossman, Sanford, 1975. "Rational expectations and the econometric modeling of markets subject to uncertainty : A Bayesian approach," Journal of Econometrics, Elsevier, vol. 3(3), pages 255-272, August.
    25. Zellner, Arnold & Israilevich, Guillermo, 2005. "The Marshallian macroeconomic model: A progress report," International Journal of Forecasting, Elsevier, vol. 21(4), pages 627-645.
    26. Lucas, Robert E, Jr, 1973. "Some International Evidence on Output-Inflation Tradeoffs," American Economic Review, American Economic Association, vol. 63(3), pages 326-34, June.
    27. Stock, James H & Watson, Mark W, 1996. "Evidence on Structural Instability in Macroeconomic Time Series Relations," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(1), pages 11-30, January.
    28. Diebold, Francis X & Gunther, Todd A & Tay, Anthony S, 1998. "Evaluating Density Forecasts with Applications to Financial Risk Management," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 863-83, November.
    29. Marco Del Negro & Frank Schorfheide, 2004. "Priors from General Equilibrium Models for VARS," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 45(2), pages 643-673, 05.
    30. Zellner, Arnold & Chen, Bin, 2001. "Bayesian Modeling Of Economies And Data Requirements," Macroeconomic Dynamics, Cambridge University Press, vol. 5(05), pages 673-700, November.
    31. Lucas, Robert Jr, 1976. "Econometric policy evaluation: A critique," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 1(1), pages 19-46, January.
    32. Berkowitz, Jeremy, 2001. "Testing Density Forecasts, with Applications to Risk Management," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(4), pages 465-74, October.
    33. Veloce, William & Zellner, Arnold, 1985. "Entry and empirical demand and supply analysis for competitive industries," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 459-471.
    34. Kydland, Finn E & Prescott, Edward C, 1982. "Time to Build and Aggregate Fluctuations," Econometrica, Econometric Society, vol. 50(6), pages 1345-70, November.
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