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Estimation and Testing Using Jackknife IV in Heteroskedastic Regressions with Many Weak Instruments

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Author Info
Norman R. Swanson
John C. Chao

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Abstract

This paper develops Wald-type tests for general (possibly nonlinear) restrictions in the context of a weakly-identified heteroskedastic IV regression. In particular, it is first shown that, in a framework with many weak instruments, consistency and asymptotic normality can be obtained when estimating structural parameters using JIVE, even if disturbances exhibit heteroskedasticity of unknown form. This is not the case, however, with other well-known IV estimators, such as LIML, Fuller's modified LIML, 2SLS, and B2SLS, which are shown to be inconsistent in general under heteroskedasticity. We also introduce new covariance matrix estimators for JIVE, which are consistent even when instrument weakness is such that the rate of growth of the concentration parameter, r(n), is slower than that of the number of instruments, K(n), and possibly much slower than the sample size n, provided that K(n)^0.5/r(n) goes to zero as n approaches infinity. Wald test statistics are then constructed using these covariance matrix estimators, and the resulting statistics are shown to have limiting chi-square distributions under the null hypothesis. A primary advantage of our approach is that, relative to other testing frameworks which have previously been proposed in the weak instruments literature, our framework allows one to test hypotheses more general than simple point null hypotheses. We feel that this feature, taken together with the fact that our tests are robust to heteroskedasticity of unknown form, is important from the perspective of empirical application, given that testing general linear and nonlinear restrictions are often of interest to empirical researchere, and given that heteroskedasticity is prevalent, particularly in microeconomic datasets

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Publisher Info
Paper provided by Econometric Society in its series Econometric Society 2004 Far Eastern Meetings with number 668.

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Date of creation: 11 Aug 2004
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Handle: RePEc:ecm:feam04:668

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Related research
Keywords: heteroskedasticity; Jackknife estimation; local-to-zero framework; Wald test; weak instruments;

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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
C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

References listed on IDEAS
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. James H. Stock & Motohiro Yogo, 2002. "Testing for Weak Instruments in Linear IV Regression," NBER Technical Working Papers 0284, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
  2. Douglas Staiger & James H. Stock, 1997. "Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, vol. 65(3), pages 557-586, May.
    Other versions:
  3. Frank Kleibergen, 2000. "Pivotal Statistics for Testing Structural Parameters in Instrumental Variables Regression," Tinbergen Institute Discussion Papers 00-055/4, Tinbergen Institute. [Downloadable!]
  4. John C. Chao & Norman R. Swanson, 2003. "Asymptotic Normality of Single-Equation Estimators for the Case with a Large Number of Weak Instruments," Departmental Working Papers 200312, Rutgers University, Department of Economics. [Downloadable!]
  5. John C. Chao & Norman Rasmus Swanson, 2004. "Consistent Estimation with a Large Number of Weak Instruments," Yale School of Management Working Papers ysm374, Yale School of Management. [Downloadable!]
    Other versions:
  6. H. Kelejian, Harry & Prucha, Ingmar R., 2001. "On the asymptotic distribution of the Moran I test statistic with applications," Journal of Econometrics, Elsevier, vol. 104(2), pages 219-257, September. [Downloadable!] (restricted)
  7. Blomquist, Soren & Dahlberg, Matz, 1999. "Small Sample Properties of LIML and Jackknife IV Estimators: Experiments with Weak Instruments," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(1), pages 69-88, Jan.-Feb.. [Downloadable!]
  8. Chuanming Gao & Kajal Lahiri, 2000. "A Comparison of Some Recent Bayesian and Classical Procedures for Simultaneous Equation Models with Weak Instruments," Econometric Society World Congress 2000 Contributed Papers 0230, Econometric Society. [Downloadable!]
  9. Donald, Stephen G & Newey, Whitney K, 2001. "Choosing the Number of Instruments," Econometrica, Econometric Society, vol. 69(5), pages 1161-91, September.
  10. Hahn, Jinyong, 2002. "Optimal Inference With Many Instruments," Econometric Theory, Cambridge University Press, vol. 18(01), pages 140-168, February. [Downloadable!]
  11. Smith, Richard J, 1997. "Alternative Semi-parametric Likelihood Approaches to Generalised Method of Moments Estimation," Economic Journal, Royal Economic Society, vol. 107(441), pages 503-19, March. [Downloadable!] (restricted)
  12. Whitney K. Newey & Richard J. Smith, 2004. "Higher Order Properties of Gmm and Generalized Empirical Likelihood Estimators," Econometrica, Econometric Society, vol. 72(1), pages 219-255, 01. [Downloadable!] (restricted)
    Other versions:
  13. Morimune, Kimio, 1983. "Approximate Distributions of k-Class Estimators When the Degree of Overidentifiability Is Large Compared with the Sample Size," Econometrica, Econometric Society, vol. 51(3), pages 821-41, May. [Downloadable!] (restricted)
  14. Angrist, Joshua D & Krueger, Alan B, 1995. "Split-Sample Instrumental Variables Estimates of the Return to Schooling," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(2), pages 225-35, April.
  15. 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.
  16. Marcelo J. Moreira, 2003. "A Conditional Likelihood Ratio Test for Structural Models," Econometrica, Econometric Society, vol. 71(4), pages 1027-1048, 07. [Downloadable!] (restricted)
  17. Bekker, Paul A, 1994. "Alternative Approximations to the Distributions of Instrumental Variable Estimators," Econometrica, Econometric Society, vol. 62(3), pages 657-81, May. [Downloadable!] (restricted)
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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. Paul J. Devereux & Daniel A. Ackerberg, 2006. "Comment on 'The case against JIVE'," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(6), pages 835-838. [Downloadable!]
  2. Ackerberg, Daniel & Devereux, Paul J., 2008. "Improved JIVE Estimators for Overidentified Linear Models with and without Heteroskedasticity," CEPR Discussion Papers 6926, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
    Other versions:
  3. Christian Hansen & Jerry Hausman & Whitney Newey, 2006. "Estimation with many instrumental variables," CeMMAP working papers CWP19/06, Centre for Microdata Methods and Practice, Institute for Fiscal Studies. [Downloadable!]
  4. Whitney Newey & Frank Windmeijer, 2005. "GMM with many weak moment conditions," CeMMAP working papers CWP18/05, Centre for Microdata Methods and Practice, Institute for Fiscal Studies. [Downloadable!]
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