Bayesian Modeling Of Economies And Data Requirements
AbstractMarshallian 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoArticle provided by Cambridge University Press in its journal Macroeconomic Dynamics.
Volume (Year): 5 (2001)
Issue (Month): 05 (November)
Contact details of provider:
Postal: The Edinburgh Building, Shaftesbury Road, Cambridge CB2 2RU UK
Fax: +44 (0)1223 325150
Web page: http://journals.cambridge.org/jid_MDYProvider-Email:email@example.com
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- 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.
- 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.
- Auffhammer, Maximilian & Steinhauser, Ralf, 2006. "The Future Trajectory of US CO2 Emissions: The Role of State vs. Aggregate Information," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt4878j5w0, Department of Agricultural & Resource Economics, UC Berkeley.
- Janine Aron & John Muellbauer & Coen Pretorius, 2004. "A Framework for Forecasting the Components of the Consumer Price," Development and Comp Systems 0409054, EconWPA.
- 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.
- Arnold Zellner, 2003. "Some Recent Developments in Econometric Inference," Econometric Reviews, Taylor & Francis Journals, vol. 22(2), pages 203-215.
- Zellner, Arnold, 2006. "S. James Press And Bayesian Analysis," Macroeconomic Dynamics, Cambridge University Press, vol. 10(05), pages 667-684, November.
- Kim, Kun Ho, 2011. "Density forecasting through disaggregation," International Journal of Forecasting, Elsevier, vol. 27(2), pages 394-412, April.
- 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.
- Zellner, Arnold, 2007. "Some aspects of the history of Bayesian information processing," Journal of Econometrics, Elsevier, vol. 138(2), pages 388-404, June.
- 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.
- Fildes, Robert & Stekler, Herman, 2002.
"The state of macroeconomic forecasting,"
Journal of Macroeconomics,
Elsevier, vol. 24(4), pages 435-468, December.
- Zellner, Arnold & Israilevich, Guillermo, 2005. "The Marshallian macroeconomic model: A progress report," International Journal of Forecasting, Elsevier, vol. 21(4), pages 627-645.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Keith Waters).
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.