Hostname: page-component-cd9895bd7-hc48f Total loading time: 0 Render date: 2024-12-25T06:15:31.782Z Has data issue: false hasContentIssue false

Testing Identifiability and Specification in Instrumental Variable Models

Published online by Cambridge University Press:  11 February 2009

John G. Cragg
Affiliation:
University of British Columbia
Stephen G. Donald
Affiliation:
University of Florida

Abstract

The paper develops and explores tests, based on standard moment specifications, for the identifiability of parameters apparently estimable by instrumental variables. An asymptotic expansion under standard restrictive assumptions on the error distribution suggests a correction to the asymptotic distribution. A small sampling experiment indicates that the tests are of use.

Type
Articles
Copyright
Copyright © Cambridge University Press 1993

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

1.Anderson, T.W. The asymptotic distribution of certain characteristic roots and vectors. In Neyman, J. (ed.), Proceedings of the Second Berkeley Symposium on Mathematical Statistics and Probability, pp. 103130. Berkeley: University of California Press, 1951.Google Scholar
2.Anderson, T.W.Introduction to Multivariate Statistical Analysis. New York: Wiley, 1958.Google Scholar
3.Anderson, T.W. & Rubin, H.. Estimation of the parameters of a single equation in a complete system of equations. Annals of Mathematical Statistics 20 (1949): 4663.CrossRefGoogle Scholar
4.Cragg, J.G.Identification in incomplete specifications. Discussion Paper 89–13, Department of Economics, University of British Columbia, 1989.Google Scholar
5.Cragg, J.G. & Donald, S.G.. Testing for identifiability of simultaneous-equation coefficients. Discussion Paper 89–23, Dept. of Economics, University of British Columbia, 1989.Google Scholar
6.Cragg, J.G. & Donald, S.G.. A result on testing dimensionality when the true dimension is less than the null. Mimeo, 1991.Google Scholar
7.Fujikoshi, Y.Asymptotic expansions of the distributions of the latent roots in MANOVA and the canonical correlations. Journal of Multivariate Analysis 7 (1977): 386396.CrossRefGoogle Scholar
8.Fujikoshi, Y. Asymptotic expansions for the distributions of some multivariate tests. In Krishnaiah, P.R. (ed.), Multivariate Analysis-IV, pp. 5571. Amsterdam: North-Holland, 1977.Google Scholar
9.Goldberger, A.S.Econometric Theory. New York: Wiley, 1964.Google Scholar
10.Hansen, L.P.Large sample properties of generalized method of moments estimators. Econometrica 50 (1982): 10291055.CrossRefGoogle Scholar
11.Hsu, P.L.On the limiting distribution of a determinantal equation. Journal of the London Mathematical Society 16 (1941): 183194.CrossRefGoogle Scholar
12.Koopmans, T.C. & Hood, W.C.. The estimation of simultaneous linear economic relationships. In Hood, W.C. and Koopmans, T.C. (eds.), Studies in Econometric Method, pp. 112199. New York: Wiley, 1953.Google Scholar
13.Kunimoto, N., Morimune, K. & Tsukuda, Y.. Asymptotic expansions of the distribution of the test statistics for overidentifying restrictions in a system of simultaneous equations. International Economic Review 24 (1983): 199215.CrossRefGoogle Scholar
14.Lawley, D.N.Tests of significance for the latent roots of covariance and correlation matrices. Biometrika 43 (1956): 171178.CrossRefGoogle Scholar
15.Lawley, D.N.Tests of significance in canonical analysis. Biometrika 46 (1959): 5966.CrossRefGoogle Scholar
16.Phillips, P.C.B.Partially identified econometric models. Econometric Theory 5 (1989): 151240.CrossRefGoogle Scholar
17.Rao, C.R.Linear Statistical Inference. New York: Wiley, 1965.Google Scholar
18.Rothenberg, T.J. Approximating the distribution of econometric estimators and test statistics. In Griliches, Z. and Intrilligator, M.D. (eds.), Handbook of Econometrics, Vol. II, pp. 881935. Amsterdam: Elsevier, 1984.CrossRefGoogle Scholar
19.Schmidt, P.Econometrics. New York: Marcel Dekker, 1976.Google Scholar