Hostname: page-component-848d4c4894-5nwft Total loading time: 0 Render date: 2024-05-13T12:20:50.922Z Has data issue: false hasContentIssue false
  • English
  • Français

Effect of Nonnormality on the Estimation of a Single Structural Equation with Structural Change

Published online by Cambridge University Press:  18 October 2010

Jiro Hodoshima
Jiro Hodoshima
Affiliation:
Nanzan University
Get access Rights & Permissions [Opens in a new window]

Abstract

Effect of nonnormality on the asymptotic property of three estimators of a single structural equation with structural change is examined. The three estimators are the limited information maximum likelihood estimator, derived under normality and equality of structural variances in different samples, given by Hodoshima, a two-stage least squares type estimator due to Barten and Bronsard, and a minimum distance estimator presented here. Normality is relaxed but the equality assumption of structural variances is retained. Under nonnormality the limited information maximum likelihood estimator is consistent but may not be efficient relative to the Barten and Bronsard's estimator. A sufficient condition is given under which the limited information maximum likelihood estimator dominates the Barten and Bronsard's estimator in terms of the asymptotic covariance matrix. The minimum distance estimator dominates other estimators.

Type
Articles
Information
Econometric Theory , Volume 5 , Issue 1 , April 1989 , pp. 53 - 62
Copyright
Copyright © Cambridge University Press 1989

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

REFERENCES

1.Anderson, T.W.An Introduction to Multivariate Statistical Analysis. New York: John Wiley, 1958.Google Scholar
2.Anderson, T.W. & Rubin, H.. The asymptotic properties of estimates of the parameters of a single equation in a complete system of stochastic equations. Annals of Mathematical Statistics 21 (1950): 570–582.Google Scholar
3.Barten, A.P. & Bronsard, L.S.. Two stage least-squares estimation with shifts in the structural form. Econometrica 38 (1970): 938–941.Google Scholar
4.Chamberlain, G., Panel data. In Handbook of Econometrics, Griliches, Z. & Intrili-gator, M.D. (eds.), Vol. II. Amsterdam: North-Holland, 1984.Google Scholar
5.Hansen, L.P.Large sample properties of generalized methods of moments estimators. Econometrica 50 (1982): 1029–1054.CrossRefGoogle Scholar
6.Hausman, J.A., Newey, W.K., & Taylor, W.E.. Efficient estimation and identification of simultaneous equation models with covariance restrictions. Econometrica 55 (1987): 849–874.CrossRefGoogle Scholar
7.Hodoshima, J., Estimation of a single structural equation with structural change. Econometric Theory 4 (1988): 86–96.CrossRefGoogle Scholar
8.Johnston, J.Econometric Methods (2nd ed.). New York: McGraw-Hill, 1972.Google Scholar
9.Shibata, Y.Normal Distribution. Tokyo: University of Tokyo Press (in Japanese), 1981.Google Scholar
10.White, H., Instrumental variables regression with independent observations. Econometrica 50 (1982): 483–499.CrossRefGoogle Scholar
11.Wilks, S.S.Mathematical Statistics. New York: John Wiley, 1962.Google Scholar