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Journal of Economics and Finance

, Volume 23, Issue 1, pp 39–44 | Cite as

An error-correction model of the demand for equity mutual funds in the U.S. 1973–1994

  • Nelson C. Modeste
  • Muhammad Mustafa
Article

Abstract

The purpose of this paper is to estimate an error-correction model of the U.S. demand for equity mutual funds. Using annual data for the period 1973–1994, this study finds that changes in the demand for equity mutual funds have been significantly influenced by the changes in the rate of return on equity mutual funds and savings deposits, as well as by the growth in income over the long run.

Keywords

Ordinary Little Square Mutual Fund Instrumental Variable Regression Constant Dollar Saving Deposit 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Board of Governors of the Federal Reserve System.Federal Reserve Bulletin. Washington, D.C. Various Issues.Google Scholar
  2. Breusch, T. S. 1978. “Testing for Autocorrelation in Dynamic Linear Models”.Australian Economic Papers 17: 334–55.Google Scholar
  3. Davidson, R., and J. G. MacKinnon. 1993.Estimation and Inference in Econometrics. New York: Oxford University Press.Google Scholar
  4. Dickey, D.A., and W.A. Fuller. 1981. “Likelihood Ratio Statistics For Autoregressive Time Series With a Unit Root.”Econometrics 49: 1057–1072.CrossRefGoogle Scholar
  5. Engle, R.F., and C.W.J. Granger. 1987. “Cointegration and Error-Correction: Representation, Estimation and Testing.”Econometrics 55: 251–76.CrossRefGoogle Scholar
  6. Godfrey, L.G. 1978. “Testing Against General Autoregressive and Moving Average Error Models When the Regressors Include Lagged Dependent Variables.”Econometrica 46: 1293–1301.CrossRefGoogle Scholar
  7. Griffiths, W.E., R.C. Hill, and G.G. Judge. 1993.Learning and Practicing Econometrics. New York: John Wiley.Google Scholar
  8. International Monetary Fund. 1995.International Financial Statistics Yearbook 1995. Washington, D.C.Google Scholar
  9. Jenkinson, T.J. 1986. “Testing Neo-Classical Theories of Labour Demand: An Application of Cointegration Techniques.”Oxford Bulletin of Economics and Statistics 48: 241–51.CrossRefGoogle Scholar
  10. Johansen, S., and K. Juselius. 1990. “Maximum Likelihood Estimation and Inference on Cointegration—With Applications to the Demand for Money.”Oxford Bulletin of Economics and Statistics 52: 169–203.CrossRefGoogle Scholar
  11. Ljung, G.M., and G.E.P. Box. 1978. “On a Measure of Lack of Fit in Time Series Model.”Biometrika 65: 297–303.CrossRefGoogle Scholar
  12. Mack, P.R. 1993. “Recent Trends in the Mutual Fund Industry.”Federal Reserve Bulletin 79: 1001–1012.Google Scholar
  13. Mehra, Y.P. 1991. “An Error-Correction Model of U.S. M2 Demand.” Federal Reserve Bank of Richmond,Economic Review 77: 3–12.Google Scholar
  14. — 1993. “The Stability of the M2 Demand Function: Evidence from an Error-Correction Model.”Journal of Money, Credit, and Banking 25: 455–460.CrossRefGoogle Scholar
  15. U.S. Bureau of the Census.Statistical Abstract of the United States. Washington, D.C. Various Issues.Google Scholar
  16. U.S. President. 1995.Economic Report of the President. Washington, D.C.: United States Government Printing Office.Google Scholar
  17. Schwarz, G. 1978. “Estimating the Dimension of a Model.”Annals of Statistics 6: 461–64.Google Scholar
  18. Small, D.H., and R.D. Porter. 1989. “Understanding the Behavior of M2 and V2.”Federal Reserve Bulletin 75: 244–52.Google Scholar
  19. Swamy, P.A.V.B., and J.S. Mehta. 1996. “Cointegration and Error-Correction Models: Are They For Real?”Journal of Applied Statistical Science 3: 25–34.Google Scholar

Copyright information

© Springer 1999

Authors and Affiliations

  • Nelson C. Modeste
    • 1
  • Muhammad Mustafa
    • 1
  1. 1.Department of Agribusiness and EconomicsSouth Carolina State UniversityOrangeburg

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