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Assessing Instrumental Variable Relevance:An Alternative Measure and Some Exact Finite Sample Theory

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Author Info
D.S. Poskitt
C.L. Skeels

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Abstract

focus on the ability of the instrument set to predict a single endogenous regressor, even if there is more than one endogenous regressor in the equation of interest. We propose new measures of instrument relevance in the presence of multiple endogenous regressors, taking both univariate and multivariate perspectives, and develop the accompanying exact finite sample distribution theory in each case. In passing, the paper also explores relationships that exist between the measures proposed here and other statistics that have been proposed elsewhere in the literature. These explorations highlight the close connection between notions of instrument relevance, identification and specification testing in simultaneous equations models.

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File URL: http://www.economics.unimelb.edu.au/SITE/research/workingpapers/wp02/862.pdf
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Publisher Info
Paper provided by The University of Melbourne in its series Department of Economics - Working Papers Series with number 862.

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Length: 26 pages
Date of creation: 2002
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Handle: RePEc:mlb:wpaper:862

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Related research
Keywords: Instrumental variables; weak instruments; relevance; alienation; Wilks’ Lambda.;

Find related papers by JEL classification:
C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Hypothesis Testing
C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation
C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Econometric and Statistical Methods; Specific Distributions
C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

References listed on IDEAS
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  1. Woglom, Geoffrey, 2001. "More Results on the Exact Small Sample Properties of the Instrumental Variable Estimator," Econometrica, Econometric Society, vol. 69(5), pages 1381-89, September.
  2. Alastair Hall & Fernanda P. M. Peixe, 2000. "A Consistent Method for the Selection of Relevant Instruments," Econometric Society World Congress 2000 Contributed Papers 0790, Econometric Society. [Downloadable!]
  3. Jinyong Hahn & Jerry Hausman, 2002. "A New Specification Test for the Validity of Instrumental Variables," Econometrica, Econometric Society, vol. 70(1), pages 163-189, January. [Downloadable!] (restricted)
    Other versions:
  4. Douglas Staiger & James H. Stock, 1997. "Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, vol. 65(3), pages 557-586, May.
    Other versions:
  5. Jean-Marie Dufour, 1997. "Some Impossibility Theorems in Econometrics with Applications to Structural and Dynamic Models," Econometrica, Econometric Society, vol. 65(6), pages 1365-1388, November.
  6. Peter C.B. Phillips, 1987. "Partially Identified Econometric Models," Cowles Foundation Discussion Papers 845R, Cowles Foundation, Yale University, revised Aug 1988. [Downloadable!]
  7. Charles Nelson & Richard Startz & Eric Zivot, 2000. "Improved Inference for the Instrumental Variables Estimator," Econometric Society World Congress 2000 Contributed Papers 1600, Econometric Society. [Downloadable!]
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  8. Stock, James H & Wright, Jonathan H & Yogo, Motohiro, 2002. "A Survey of Weak Instruments and Weak Identification in Generalized Method of Moments," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(4), pages 518-29, October.
  9. Forchini, G. & Hillier, G.H., 1999. "Conditional Inference for Possibly Unidentified Structural Equations," Discussion Paper Series In Economics And Econometrics 9906, Economics Division, School of Social Sciences, University of Southampton.
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  10. In Choi & Peter C.B. Phillips, 1989. "Asymptotic and Finite Sample Distribution Theory for IV Estimators and Tests in Partially Identified Structural Equations," Cowles Foundation Discussion Papers 929, Cowles Foundation, Yale University. [Downloadable!]
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  11. Jiahui Wang & Eric Zivot, 1998. "Inference on Structural Parameters in Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, vol. 66(6), pages 1389-1404, November.
  12. Charles R. Nelson & Richard Startz, 1988. "Some Further Results on the Exact Small Sample Properties of the Instrumental Variable Estimator," NBER Technical Working Papers 0068, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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  13. Hall, Alastair R & Rudebusch, Glenn D & Wilcox, David W, 1996. "Judging Instrument Relevance in Instrumental Variables Estimation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 37(2), pages 283-98, May.
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Full references

Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. D. S. Poskitt & C. L. Skeels, 2004. "Approximating the Distribution of the Instrumental Variables Estimator when the Concentration Parameter is Small," Monash Econometrics and Business Statistics Working Papers 19/04, Monash University, Department of Econometrics and Business Statistics. [Downloadable!]
  2. Grant Hillier & Giovanni Forchini, 2004. "Ill-posed Problems and Instruments' Weakness," Econometric Society 2004 Australasian Meetings 357, Econometric Society. [Downloadable!]
  3. Agnes S. Joseph & Jan F. Kiviet, 2004. "Viewing the Relative Efficiency of IV Estimators in Models with Lagged and Instantaneous Feedbacks," Tinbergen Institute Discussion Papers 04-056/4, Tinbergen Institute. [Downloadable!]
    Other versions:
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