Skip to main content Accessibility help
×
Hostname: page-component-77c89778f8-gq7q9 Total loading time: 0 Render date: 2024-07-16T19:46:28.721Z Has data issue: false hasContentIssue false

23 - The Marshallian macroeconomic model (2000)

Published online by Cambridge University Press:  24 October 2009

Arnold Zellner
Affiliation:
Professor, Emeritus of Economics and Statistics, Graduate School of Business, University of Chicago, Chicago, IL
Arnold Zellner
Affiliation:
University of Chicago
Franz C. Palm
Affiliation:
Universiteit Maastricht, Netherlands
Get access

Summary

In this paper, background information on the origins and features of the Marshallian Macroeconomic Model (MMM) are presented. MMMs based on two alternative production functions are presented and compared. In addition, some empirical forecasting results for one of them are reviewed. Last, attention is focused on further development and implementation of the MMM.

Introduction

It is an honor and a pleasure to present my paper at this research conference honoring Professor Ryuzo Sato, a superb colleague and most productive scholar. His outstanding research analyzing production and technological change, Sato (1999a, 1999b) has been appreciated worldwide. Indeed, these topics play a central role in almost all models of industrial sectors and economies, including the models to be discussed below.

On the origins of the [Marshallian Macroeconomic Model] (MMM), in my experience it was a pleasure teaching undergraduate and graduate students the properties and uses of the Marshallian model of a competitive industry. On the other hand, teaching students macroeconomics was quite a different matter since there was no such comparable, operationally successful model available (See, e.g., Belongia and Garfinkel 1992 for an excellent review of alternative macroeconomic models, including monetarist, neo-monetarist, Keynesian, post-Keynesian, and real business cycle models and Fair 1992 and Zellner 1992, who pointed out that not enough empirical testing of alternative models had been done and more is needed to produce macroeconomic models that explain the past, predict well, and are useful in making policy.)

Type
Chapter
Information
Publisher: Cambridge University Press
Print publication year: 2004

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Belongia, M. and M. Garfinkel (eds.) (1992), “The business cycle: theories and evidence,” Proceedings of the 16th Annual Economic Policy Conference of the Federal Reserve Bank of St. Louis (Boston, Kluwer Academic)
Fair, R. (1992), “How might the debate be resolved?,” in M. Belongia and M. Garfinkel (eds.), “The business cycle: theories and evidence,” Proceedings of the 16th Annual Economic Policy Conference of the Federal Reserve Bank of St. Louis (Boston, Kluwer Academic),
Garcia-Ferrer, A., Highfield, R. A., Palm, F. C., and Zellner, A. (1987), “Macroeconomic forecasting using pooled international data,” Journal of Business and Economic Statistics 5(1), 53–67; chapter 13 in this volumeGoogle Scholar
Greene, W. (1993), Econometric Analysis, 2nd edn. (New York, Macmillan)
Hong, C. (1989), “Forecasting real output growth rates and cyclical properties of models: a Bayesian approach,” PhD thesis, Department of Economics, University of Chicago
Min, C. (1992), “Economic analysis and forecasting of international growth rates using Bayesian techniques,” PhD thesis, Department of Economics, University of Chicago
Muth, J. (1961), “Rational expectations and the theory of price movements,” Econometrica 29, 315–35CrossRefGoogle Scholar
Palm, F. C. (1976), “Testing the dynamic specification of an econometric model with an application to Belgian data,” European Economic Review 8, 269–89CrossRefGoogle Scholar
Palm, F. C. (1977), “On univariate time series methods and simultaneous equation econometric models,” Journal of Econometrics 5, 379–88CrossRefGoogle Scholar
Palm, F. C. (1983), “Structural econometric modeling and time series analysis: an integrated approach,” in A. Zellner (ed.), Applied Time Series Analysis of Economic Data (Washington, DC: US Bureau of the Census, Department of Commerce), 199–233; chapter 3 in this volume
Quintana, J., Chopra, V., and Putnam, B. (1995), “Global asset allocation: stretching returns by shrinking forecasts,” in Proceedings of the Section on Bayesian Statistical Science, American Statistical Association, available at www.amstat.orgGoogle Scholar
Sato, R. (1999a), Theory of Technical Change and Economic Invariance (Cheltenham, Edward Elgar); reprint, with amendments, of 1981 edn
Sato, R. (1999b), Production, Stability and Dynamic Symmetry: The Selected Essays of Ryuzo Sato, in Economists of the Twentieth Century Series (Cheltenham, Edward Elgar)
Veloce, W. and Zellner, A. (1985), “Entry and empirical demand and supply analysis for competitive industries,” Journal of Econometrics 30, 459–71CrossRefGoogle Scholar
Zellner, A. (1979), “Statistical analysis of econometric models,” invited paper with discussion, Journal of the American Statistical Association 74, 628–51; chapter 2 in this volumeCrossRefGoogle Scholar
Zellner, A. (1984), Basic Issues in Econometrics (Chicago, University of Chicago Press); reprinted in 1987
Zellner, A. (1992), “Comment on Ray Fair's thoughts on ‘How might the debate be resolved?’,” in M. Belongia and M. Garfinkel (eds.), “The business cycle: theories and evidence,” Proceedings of the 16th Annual Economic Policy Conference of the Federal Reserve Bank of St. Louis (Boston, Kluwer Academic), 148–57
Zellner, A. (1994), “Time series analysis, forecasting, and econometric modeling: the structural econometric modeling, time series analysis (SEMTSA) approach,” Journal of Forecasting 13, 215–33, invited paper with discussion; chapter 4 in this volumeCrossRefGoogle Scholar
Zellner, A. (1997), Bayesian Analysis in Econometrics and Statistics: The Zellner View and Papers (Cheltenham, Edward Elgar)
Zellner, A. (1999), “Bayesian and non-Bayesian approaches to scientific modeling in economics and econometrics,” invited keynote paper presented at the Ajou University Research Conference in Honor of Professor Tong Hun Lee, South Korea, August; [published in Special Issue of the Korean Journal of Money and Finance 5(2) (November 2000)]
Zellner, A. and B. Chen (2000), “Bayesian modeling of economies and data requirements,” invited keynote address at meeting of the International Institute of Forecasters and International Journal of Forecasting, Lisbon June; [published in Macroeconomic Dynamics 5(2001), 673–700]; chapter 24 in this volume
Zellner, A. and Hong, C. (1989), “Forecasting international growth rates using Bayesian shrinkage and other procedures,” Journal of Econometrics, Annals 40, 183–202; chapter 14 in this volumeCrossRefGoogle Scholar
Zellner, A. and Min, C. (1999), “Forecasting turning points in countries' output growth rates: a response to Milton Friedman,” Journal of Econometrics 88, 203–6; chapter 19 in this volumeCrossRefGoogle Scholar
Zellner, A. and Palm, F. C. (1974), “Time series analysis and simultaneous equation econometric models,” Journal of Econometrics 2, 17–54; chapter 1 in this volumeCrossRefGoogle Scholar
Zellner, A. and Palm, F. C. (1975), “Time series analysis of structural monetary models of the US economy,” Sankyā: The Indian Journal of Statistics, Series C 37, 12–56; chapter 6 in this volumeGoogle Scholar
Zellner, A. and Revankar, N. (1969), “Generalized production functions,” The Review of Economic Studies 36, 241–50CrossRefGoogle Scholar
Zellner, A. and Ryu, H. (1998), “Alternative functional forms for production, cost and returns to scale functions,” Journal of Applied Econometrics 13, 101–273.0.CO;2-V>CrossRefGoogle Scholar

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

  • The Marshallian macroeconomic model (2000)
    • By Arnold Zellner, Professor, Emeritus of Economics and Statistics, Graduate School of Business, University of Chicago, Chicago, IL
  • Edited by Arnold Zellner, University of Chicago, Franz C. Palm, Universiteit Maastricht, Netherlands
  • Book: The Structural Econometric Time Series Analysis Approach
  • Online publication: 24 October 2009
  • Chapter DOI: https://doi.org/10.1017/CBO9780511493171.024
Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

  • The Marshallian macroeconomic model (2000)
    • By Arnold Zellner, Professor, Emeritus of Economics and Statistics, Graduate School of Business, University of Chicago, Chicago, IL
  • Edited by Arnold Zellner, University of Chicago, Franz C. Palm, Universiteit Maastricht, Netherlands
  • Book: The Structural Econometric Time Series Analysis Approach
  • Online publication: 24 October 2009
  • Chapter DOI: https://doi.org/10.1017/CBO9780511493171.024
Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • The Marshallian macroeconomic model (2000)
    • By Arnold Zellner, Professor, Emeritus of Economics and Statistics, Graduate School of Business, University of Chicago, Chicago, IL
  • Edited by Arnold Zellner, University of Chicago, Franz C. Palm, Universiteit Maastricht, Netherlands
  • Book: The Structural Econometric Time Series Analysis Approach
  • Online publication: 24 October 2009
  • Chapter DOI: https://doi.org/10.1017/CBO9780511493171.024
Available formats
×