Testing for weak instruments in Linear Iv regression

Identification and Inference for Econometric Models: Essays in Honor of Thomas Rothenberg

Volumn , Issue , 2005, Pages 80-108

  Stock, James H ^a   Yogo, Motohiro ^b  

^a HARVARD UNIVERSITY   (United States)

^b UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84926100534     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1017/CBO9780511614491.006     Document Type: Chapter

References (20)

1. Bound, J., D. A. Jaeger, and R. M. Baker (1995), “Problems with Instrumental Variables Estimation when the Correlation Between the Instruments and the Endogenous Explanatory Variable is Weak,” Journal of the American Statistical Association, 90, 443–50.
2. Bowden, R., and D. Turkington (1984), Instrumental Variables. Cambridge: Cambridge University Press.
3. Chao, J. C., and N. R. Swanson (2002), “Consistent Estimation with a Large Number of Weak Instruments,” unpublished manuscript, University of Maryland.
4. Cragg, J. G., and S. G. Donald (1993), “Testing Identifiability and Specification in Instrumental Variable Models,” Econometric Theory, 9, 222–40.
5. Donald, S. G, and W. K. Newey (2001), “Choosing the Number of Instruments,” Econometrica, 69, 1161–91.
6. Fuller, W. A. (1977): “Some Properties of a Modification of the Limited Information Estimator,” Econometrica, 45, 939–53.
7. Hall, A., G. D. Rudebusch, and D. Wilcox (1996), “Judging Instrument Relevance in Instrumental Variables Estimation,” International Economic Review, 37, 283–98.
8. Hahn, J., and J. Hausman (2002), “A New Specification Test for the Validity of Instrumental Variables,” Econometrica, 70, 163–89.
9. Hahn, J., and J. Hausman (2003), “Weak Instruments: Diagnosis and Cures in Empirical Econometrics,” American Economic Review, Papers and Proceedings, 93, 118–25.
10. Muirhead, R. J. (1978), “Latent Roots and Matrix Variates: A Review of Some Asymptotic Results,” Annals of Statistics, 6, 5–33.
11. Muirhead, R. J. (1982), Aspects of Multivariate Statistical Theory. New York: Wiley.
12. Nagar, A. L. (1959), “The Bias and Moment Matrix of the General k-Class Estimators of the Parameters in Simultaneous Equations,” Econometrica, 27, 575–95.
13. Nelson, C. R., and R. Startz (1990a), “Some Further Results on the Exact Small Sample Properties of the Instrumental Variables Estimator,” Econometrica, 58, 967–76.
14. Nelson, C. R., and R. Startz (1990b), “The Distribution of the Instrumental Variables Estimator and Its t-Ratio when the Instrument is a Poor One,” Journal of Business, 63, 5125–40.
15. Rothenberg, T. J. (1983), “Asymptotic Properties of Some Estimators in Structural Models,” in (ed. by S. Karlin, T. Amemiya, and L. Goodman), Studies in Econometrics, Time Series, and Multivariate Statistics. Orlando: Academic Press.
16. Rothenberg, T. J. (1984), “Approximating the Distributions of Econometric Estimators and Test Statistics,” Chapter 15 in Handbook of Econometrics, Vol. 2 (ed. by Z. Griliches and M. D. Intriligator), Amsterdam: North Holland, 881–935.
17. Shea, J. (1997), “Instrument Relevance in Multivariate Linear Models: A Simple Measure,” Review of Economics and Statistics, 79, 348–52.
18. Staiger, D., and J. H. Stock (1997): “Instrumental Variables Regression with Weak Instruments,” Econometrica, 65, 557–86.
19. Stock, J. H., J. H. Wright, and M. Yogo (2002), “A Survey of Weak Instruments and Weak Identification in Generalized Method of Moments,” Journal of Business and Economic Statistics, 20, 518–29.
20. Stock, J. H., and M. Yogo (2005), “Asymptotic Distributions of Instrumental Variables Statistics with Many Weak Instruments,” Chapter 6 in this volume