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THE USE OF A MARSHALLIAN MACROECONOMIC MODEL FOR POLICY EVALUATION: CASE OF SOUTH AFRICA

Published online by Cambridge University Press:  23 March 2012

Jacques Kibambe Ngoie  and
Arnold Zellner
Jacques Kibambe Ngoie*
Affiliation:
University of Chicago and University of Pretoria
Arnold Zellner
Affiliation:
University of Chicago
*
Address correspondence to: Jacques Kibambe Ngoie, Department of Economics, University of Pretoria, Lynwood Rd, Pretoria 0002, South Africa; e-mail: Jacques.kibambe@up.ac.za.
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Abstract

Using a disaggregated Marshallian macroeconomic model, this paper investigates how the adoption of a set of “free market reforms” may affect the economic growth rate of South Africa. Our findings suggest that the institution of the proposed policy reforms would yield substantial growth in aggregate annual real GDP. The resulting annual GDP growth rate could range from 5.3% to 9.8%, depending on which variant of the reform policies was implemented.

Keywords

Marshallian Macroeconomic ModelDisaggregationTransfer FunctionsMacroeconomic Policy Analysis

Information

Type
Articles
Information
Macroeconomic Dynamics , Volume 16 , Issue 3 , June 2012 , pp. 423 - 448
Copyright
Copyright © Cambridge University Press 2012

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References

REFERENCES

Akaike, H. (1973) Maximum likelihood identification of Gaussian autoregressive-moving average models. Biometrika 60, 255–265.Google Scholar
Box, G.E.P. and Jenkins, G.M. (1970) Time Series Analysis: Forecasting and Control. San Francisco: Holden-Day.Google Scholar
De Alba, E. and Zellner, A. (1991) Aggregation, Disaggregation, Predictive Precision and Modeling. Working paper, HGB Alexander Research Foundation, University of Chicago.Google Scholar
Jeffreys, H. (1967) Theory of Probability, 3rd ed., Oxford, UK: Oxford University Press.Google Scholar
Judge, G.G., Griffiths, W.E., Hill, R.C., Lütkepohl, H., and Lee, T.C. (1985) The Theory and Practice of Econometrics (2nd ed.), New York: John Wiley & Sons.Google Scholar
Kim, K.H. (2007) To Aggregate or Disaggregate? Empirical Evidence of Forecasting Improvements by Data Disaggregation. PhD Dissertation, University of Chicago.Google Scholar
Ngoie, K.J., van Eyden, R., and duToit, C.B. (2009) Social Ingredients in the Study of Sectoral Growth. Working paper 151, Economic Research Southern Africa.Google Scholar
Quenouille, M.H. (1957) The Analysis of Multiple Time Series. London: Charles Griffin.Google Scholar
Theil, H. (1979) A differential approach to input–output analysis. Economic Letters 3, 381–385.Google Scholar
Tinbergen, J. (1956) Economic Policy: Principles and Design. Amsterdam: North-Holland.Google Scholar
Veloce, W. and Zellner, A. (1985) Entry and empirical demand and supply analysis for competitive industries. Journal of Econometrics 30, 459–471.Google Scholar
Zellner, A. (1962) An efficient method of estimating seemingly unrelated. Regressions and tests for aggregation bias. Journal of the American Statistical Association 57, 348–368.Google Scholar
Zellner, A. and Ando, T. (2010) A direct Monte Carlo approach for Bayesian analysis of the seemingly unrelated regression model. Journal of Econometrics 159, 33–45.Google Scholar
Zellner, A. and Chen, B. (2001) Bayesian modeling of economics and data requirements. Macroeconomic Dynamics 5, 673–700.Google Scholar
Zellner, A. and Israilevich, G. (2005) The Marshallian macroeconomic model: A progress report. Macroeconomic Dynamics 9, 220–243.Google Scholar
Zellner, A. and Palm, F.C. (2004) The Structural Econometric Modeling, Time Series Analysis (SEMTSA) Approach. Cambridge, UK: Cambridge University Press.Google Scholar
Zellner, A. and Tobias, J. (2000) A note on aggregation, disaggregation and forecasting performance. Journal of Forecasting 19, 457–469.Google Scholar