Hostname: page-component-586b7cd67f-tf8b9 Total loading time: 0 Render date: 2024-11-23T21:25:25.543Z Has data issue: false hasContentIssue false

EXACT PROPERTIES OF THE CONDITIONAL LIKELIHOOD RATIO TEST IN AN IV REGRESSION MODEL

Published online by Cambridge University Press:  01 August 2009

Grant Hillier*
Affiliation:
CeMMAP and University of Southampton
*
*Address correspondence to Grant Hillier, Department of Economics, University of Southampton, Highfield, Southampton SO17 1BJ, United Kingdom; email: [email protected]

Abstract

For a simplified structural equation/IV regression model with one right-side endogenous variable, we derive the exact conditional distribution function of Moreira's (2003) conditional likelihood ratio (CLR) test statistic. This is used to obtain the critical value function needed to implement the CLR test, and reasonably comprehensive graphical versions of this function are provided for practical use. The analogous functions are also obtained for the case of testing more than one right-side endogenous coefficient, but in this case for a similar test motivated by, but not generally the same as, the likelihood ratio test. Next, the exact power functions of the CLR test, the Anderson-Rubin test, and the Lagrange multiplier test suggested by Kleibergen (2002) are derived and studied. The CLR test is shown to clearly conditionally dominate the other two tests for virtually all parameter configurations, but no test considered is either inadmissable or uniformly superior to the other two. The unconditional distribution function of the likelihood ratio test statistic is also derived using the same argument. This shows that both exactly, and under Staiger/Stock weak-instrument asymptotics, the test based on the usual asymptotic critical value is always oversized and can be very seriously so when the number of instruments is large.

Type
ARTICLES
Copyright
Copyright © Cambridge University Press 2009

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

Abramowitz, M. & Stegun, I. (1972) Handbook of Mathematical Functions. Dover.Google Scholar
Anderson, T.W. & Rubin, H. (1949) Estimation of the parameters of a single equation in a complete system of stochastic equations. Annals of Mathematical Statistics 20, 4663.Google Scholar
Andrews, D.W.K., Moreira, M., & Stock, J. (2006) Optimal two-sided invariant similar tests for instrumental variables regression. Econometrica 74, 715752.Google Scholar
Andrews, D.W.K., & Stock, J. (2005) Inference with Weak Instruments. Invited paper presented at the Econometric Society World Congress, London, 2005, and Mimeo, Harvard University.CrossRefGoogle Scholar
Breusch, T. (1986) Hypothesis testing in unidentified models. Review of Economic Studies 53, 4 (Special issue on Econometrics).Google Scholar
Chamberlain, G. (2005) Decision Theory Applied to an Instrumental Variables Model. Mimeo, Harvard University.Google Scholar
Chao, J.C. & Swanson, N.R. (2005) Consistent estimation with a large number of weak instruments. Econometrica 73, 16731692.Google Scholar
Chernozhukov, V., Hansen, C., & Jansson, M. (2006) Admissible Invariant Similar Tests for Instrumental Variables Regression. Manuscript, M.I.T.CrossRefGoogle Scholar
Forchini, G. & Hillier, G.H. (2003) Conditional inference for possibly unidentified structural equations. Econometric Theory 19, 707743.Google Scholar
Ghosh, B.K. (1970) Sequential Tests of Statistical Hypotheses. Addison-Wesley.Google Scholar
Han, C. & Phillips, P.C.B. (2006) GMM with many moment conditions. Econometrica 74, 147192.Google Scholar
Hansen, C., Hausman, J., & Newey, W. (2006) Estimation with many Instrumental Variables. CeMMAP Working paper CWP19/06, September 2006.Google Scholar
Hillier, G.H. (1987a) Hypothesis Testing in a Structural Equation; Part I: Reduced Form Equivalences and Invariant Test Procedures. Unpublished manuscript, Monash University.Google Scholar
Hillier, G.H. (1987b) Classes of similar regions and their power properties for some econometric testing problems. Econometric Theory 3, 144.Google Scholar
Hillier, G.H. (1990) On the normalization of structural equations: Properties of direction estimators. Econometrica 58, 11811194.Google Scholar
Hillier, G.H. (2005) Hypothesis Testing in a Structural Equation: Similar and Invariant Tests. Mimeo, University of Southampton.Google Scholar
Hillier, G.H. (2006a) Yet more on the exact properties of IV estimators. Econometric Theory 22, 913931.Google Scholar
Hillier, G.H. (2006b) On the Conditional Likelihood Ratio Test for Several Parameters in IV Regression. CeMMAP Working paper CW/2607, October 2006.Google Scholar
Kleibergen, F. (2002) Pivotal statistics for testing structural parameters in instrumental variables regression. Econometrica 70, 17811803.Google Scholar
Kleibergen, F. (2007) Generalizing weak instrument robust IV statistics towards multiple parameters, unrestricted covariance matrices and identification statistics. Journal of Econometrics 139, 181216.CrossRefGoogle Scholar
Moreira, M. (2003) A conditional likelihood ratio test for structural models. Econometrica 71, 10271048.Google Scholar
Phillips, P.C.B. (1983) Exact small sample theory in the simultaneous equation model. In Intriligator, M.D. and Griliches, Z., eds., Handbook of Econometrics. North Holland.Google Scholar
Phillips, P.C.B. (1989) Partially identified econometric models. Econometric Theory 5, 181240.Google Scholar
Phillips, P.C.B. (2006) A remark on bimodality and weak instrumentation in structural equation estimation. Econometric Theory 22, 947960.Google Scholar
Slater, L.J. (1960) Confluent Hypergeometric Functions. Cambridge University Press.Google Scholar
Staiger, D. & Stock, J.H. (1997) Instrumental variables regression with weak instruments. Econometrica 65, 557586.CrossRefGoogle Scholar
Stock, J.H., Wright, J.H., & Yogo, M. (2002) A survey of weak instruments and weak identification in generalized method of moments. Journal of Business and Economic Statistics 20, 518529.Google Scholar