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Bayesian Modeling Of Economies And Data Requirements

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  • Zellner, Arnold
  • Chen, Bin

Abstract

Marshallian demand, supply, and entry models are employed for major sectors of an economy that can be combined with factor market models for money, labor, capital, and bonds to provide a Marshallian macroeconomic model (MMM). Sectoral models are used to produce sectoral output forecasts, which are summed to provide forecasts of annual growth rates of U.S. real GDP. These disaggregative forecasts are compared to forecasts derived from models implemented with aggregate data. The empirical evidence indicates that it pays to disaggregate, particularly when employing Bayesian shrinkage forecasting procedures. Further, some considerations bearing on alternative model-building strategies are presented using the MMM as an example and describing its general properties. Last, data requirements for implementing MMMs are discussed.

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Bibliographic Info

Article provided by Cambridge University Press in its journal Macroeconomic Dynamics.

Volume (Year): 5 (2001)
Issue (Month): 05 (November)
Pages: 673-700

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Handle: RePEc:cup:macdyn:v:5:y:2001:i:05:p:673-700_03

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Cited by:
  1. Zellner, Arnold, 2010. "Bayesian shrinkage estimates and forecasts of individual and total or aggregate outcomes," Economic Modelling, Elsevier, vol. 27(6), pages 1392-1397, November.
  2. Patton, Andrew J. & Timmermann, Allan, 2007. "Properties of optimal forecasts under asymmetric loss and nonlinearity," Journal of Econometrics, Elsevier, vol. 140(2), pages 884-918, October.
  3. Kim, Kun Ho, 2011. "Density forecasting through disaggregation," International Journal of Forecasting, Elsevier, vol. 27(2), pages 394-412, April.
  4. Zellner, Arnold, 2006. "S. James Press And Bayesian Analysis," Macroeconomic Dynamics, Cambridge University Press, vol. 10(05), pages 667-684, November.
  5. Janine Aron & John Muellbauer & Coen Pretorius, 2004. "A Framework for Forecasting the Components of the Consumer Price," Development and Comp Systems 0409054, EconWPA.
  6. Auffhammer, Maximilian & Steinhauser, Ralf, 2006. "The future trajectory of US CO2 emissions : the role of state vs. aggregate information," CUDARE Working Paper Series 1015, University of California at Berkeley, Department of Agricultural and Resource Economics and Policy.
  7. Jacques Kibambe Ngoie & Arnold Zellner, 2012. "Modeling and policy analysis for the U.S. Science Sector," Working Papers 264, Economic Research Southern Africa.
  8. Fildes, Robert & Stekler, Herman, 2002. "The state of macroeconomic forecasting," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 435-468, December.
  9. Atkinson, Scott E. & Dorfman, Jeffrey H., 2005. "Bayesian measurement of productivity and efficiency in the presence of undesirable outputs: crediting electric utilities for reducing air pollution," Journal of Econometrics, Elsevier, vol. 126(2), pages 445-468, June.
  10. Arnold Zellner, 2003. "Some Recent Developments in Econometric Inference," Econometric Reviews, Taylor & Francis Journals, vol. 22(2), pages 203-215.
  11. Zellner, Arnold & Ando, Tomohiro, 2010. "A direct Monte Carlo approach for Bayesian analysis of the seemingly unrelated regression model," Journal of Econometrics, Elsevier, vol. 159(1), pages 33-45, November.
  12. Arnold Zellner, 2009. "Comments on “Limits of Econometrics” by David Freedman," International Econometric Review (IER), Econometric Research Association, vol. 1(1), pages 28-32, April.
  13. Zellner, Arnold, 2007. "Some aspects of the history of Bayesian information processing," Journal of Econometrics, Elsevier, vol. 138(2), pages 388-404, June.
  14. Zellner, Arnold & Israilevich, Guillermo, 2005. "The Marshallian macroeconomic model: A progress report," International Journal of Forecasting, Elsevier, vol. 21(4), pages 627-645.

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