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

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  • D.S. Poskitt
  • C.L. Skeels

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/downloads/wpapers-02/862.pdf
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Bibliographic 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
Date of revision:
Handle: RePEc:mlb:wpaper:862

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Postal: Department of Economics, The University of Melbourne, 5th Floor, Economics and Commerce Building, Victoria, 3010, Australia
Phone: +61 3 8344 5289
Fax: +61 3 8344 6899
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Web page: http://www.economics.unimelb.edu.au
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Related research

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

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References

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  1. Forchini, Giovanni & Hillier, Grant, 2003. "Conditional Inference For Possibly Unidentified Structural Equations," Econometric Theory, Cambridge University Press, vol. 19(05), pages 707-743, October.
  2. 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.
  3. Peter C.B. Phillips, 1987. "Partially Identified Econometric Models," Cowles Foundation Discussion Papers 845R, Cowles Foundation for Research in Economics, Yale University, revised Aug 1988.
  4. Alastair Hall & Fernanda Peixe, 2003. "A Consistent Method for the Selection of Relevant Instruments," Econometric Reviews, Taylor and Francis Journals, vol. 22(3), pages 269-287.
  5. Alastair R. Hall & Glenn D. Rudebusch & David W. Wilcox, 1994. "Judging instrument relevance in instrumental variables estimation," Finance and Economics Discussion Series 94-3, Board of Governors of the Federal Reserve System (U.S.).
  6. Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119, September.
  7. 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.
  8. 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.
  9. Choi, In & Phillips, Peter C. B., 1992. "Asymptotic and finite sample distribution theory for IV estimators and tests in partially identified structural equations," Journal of Econometrics, Elsevier, vol. 51(1-2), pages 113-150.
  10. Charles Nelson & Richard Startz & Eric Zivot, 2000. "Improved Inference for the Instrumental Variables Estimator," Econometric Society World Congress 2000 Contributed Papers 1600, Econometric Society.
  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. Cragg, John G. & Donald, Stephen G., 1993. "Testing Identifiability and Specification in Instrumental Variable Models," Econometric Theory, Cambridge University Press, vol. 9(02), pages 222-240, April.
  13. John Shea, 1997. "Instrument Relevance in Multivariate Linear Models: A Simple Measure," The Review of Economics and Statistics, MIT Press, vol. 79(2), pages 348-352, May.
  14. 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.
  15. Douglas Staiger & James H. Stock, 1997. "Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, vol. 65(3), pages 557-586, May.
  16. 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.
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Citations

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Cited by:
  1. Grant Hillier & Giovanni Forchini, 2004. "Ill-posed Problems and Instruments' Weakness," Econometric Society 2004 Australasian Meetings 357, Econometric Society.
  2. Joseph, Agnes S. & Kiviet, Jan F., 2005. "Viewing the relative efficiency of IV estimators in models with lagged and instantaneous feedbacks," Computational Statistics & Data Analysis, Elsevier, vol. 49(2), pages 417-444, April.
  3. George Kapetanios & Massimiliano Marcellino, 2008. "Cross-sectional Averaging and Instrumental Variable Estimation with Many Weak Instruments," Working Papers 627, Queen Mary, University of London, School of Economics and Finance.
  4. 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.

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