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Modeling Economic Growth with Unpredictable Shocks: A State-Level Application for 1960-90

Published online by Cambridge University Press:  28 April 2015

Stephan J. Goetz  and
Richard C. Ready
Stephan J. Goetz
Affiliation:
University of Kentucky
Richard C. Ready
Affiliation:
University of Kentucky
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Abstract

A Barro-type economic growth model is estimated for the 50 states in the U.S. using data for three decades beginning in 1960. Frontier estimation techniques are used to test for the presence of state-specific shocks to economic growth that are independent of the usual, normally-distributed random errors. We find that large, positive shocks to growth occurred during the period 1960-90. Our results indicate that the error term structure assumed under OLS may not be appropriate for modeling economic growth.

Keywords

economic growthfrontier estimationshocksU.S. states
Type
Articles
Information
Journal of Agricultural and Applied Economics , Volume 27 , Issue 2 , December 1995 , pp. 400 - 408
Copyright
Copyright © Southern Agricultural Economics Association 1995

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References

Aigner, D.J., Lovell, C.A.K. and Schmidt, P.. “Formulation and Estimation of Stochastic Frontier Production Functions Models,” J. Econometrics 6(1977):21–37.CrossRefGoogle Scholar
Barro, R.J., “Economic Growth in a Cross Section of Countries,” Q. J. Econ. 106(1991):407-43.CrossRefGoogle Scholar
Barro, R.J. and Sala-i-Martin, X., “Convergence Across States and Regions,” Brookings Pap. Econ. Act. no. 1 (1991): 107-82.CrossRefGoogle Scholar
Battese, G.E. “Frontier Production Functions and Technical Efficiency: A Survey of Empirical Applications in Agricultural Economics,” Agr. Econ. 7(1992): 185–208.CrossRefGoogle Scholar
Becker, G., Murphy, K. and Tamura, R., “Human Capital, Fertility, and Economic Growth,”. Polit. Econ. 98(1990): S12–37.CrossRefGoogle Scholar
Greene, B.Econometric Analysis, 2nd ed., McMillan Publishing Co., New York, 1993.Google Scholar
Jondrow, J., Lovell, C.A.K., Materov, I.S. and Schmidt, P., “On the Estimation of Technical Inefficiency in the Stochastic Frontier Production Function Model.” J. Econometrics, 19(1982):233-8.CrossRefGoogle Scholar
Jorgenson, D.W. and Yun, K.-Y., “Tax Reform and U.S. Economic Growth,” J. Polit. Econ. 98(1990): S151-93.CrossRefGoogle Scholar
King, R.G. and Rebelo, S., “Public Policy and Economic Growth: Developing Neoclassical Implications,” J. Polit. Econ. 98(1990): S126-50.CrossRefGoogle Scholar
Levine, R. and Renelt, D., “A Sensitivity Analysis of Cross-Country Growth Regression,” Amer. Econ. Rev. 82(1992):942-63.Google Scholar
Mankiw, N.G., Romer, D. and Weil, D.N., “A Contribution to the Empirics of Economic Growth,” Q. J. Econ. 107(1992):407-37.CrossRefGoogle Scholar
Nissan, E., “Convergence of Regional and State Rates of Growth of Income, Employment, and Population: 1969-2000,” Rev. Reg. Stud. 22(1992):261-76.Google Scholar
Romer, P.M., “Endogenous Technological Change,” J. Polit. Econ. 98(1990): S71–102.CrossRefGoogle Scholar
U.S. Bureau of the Census, Census of Population, Washington, DC, various years.Google Scholar
U.S. Department of Commerce, Bureau of Economic Analysis, State Personal Income by State: Estimates for 1929-89, and a Statement of Sources and Methods, Washington, DC, 1989.Google Scholar