Advanced Search
MyIDEAS: Login

Identification and inference in a simultaneous equation under alternative information sets and sampling schemes

Contents:

Author Info

  • Jan F. Kiviet

Abstract

In simple static linear simultaneous equation models the empirical distributions of IV and OLS are examined under alternative sampling schemes and compared with their first-order asymptotic approximations. We demonstrate that the limiting distribution of consistent IV is not affected by conditioning on exogenous regressors, whereas that of inconsistent OLS is. The OLS asymptotic and simulated actual variances are shown to diminish by extending the set of exogenous variables kept fixed in sampling, whereas such an extension disrupts the distribution of IV and deteriorates the accuracy of its standard asymptotic approximation, not only when instruments are weak. Against this background the consequences for the identification of parameters of interest are examined for a set- ting in which (in practice often incredible) assumptions regarding the zero correlation between instruments and disturbances are replaced by (generally more credible) inter- val assumptions on the correlation between endogenous regressor and disturbance. This yields OLS-based modified confidence intervals, which are usually conservative. Often they compare favorably with IV-based intervals and accentuate their frailty.

(This abstract was borrowed from another version of this item.)

Download Info

To our knowledge, this item is not available for download. To find whether it is available, there are three options:
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.

Bibliographic Info

Article provided by Royal Economic Society in its journal Econometrics Journal.

Volume (Year): 16 (2013)
Issue (Month): 1 (02)
Pages: S24-S59

as in new window
Handle: RePEc:wly:emjrnl:v:16:y:2013:i:1:p:s24-s59

Contact details of provider:
Postal: Office of the Secretary-General, School of Economics and Finance, University of St. Andrews, St. Andrews, Fife, KY16 9AL, UK
Phone: +44 1334 462479
Email:
Web page: http://www.res.org.uk/
More information through EDIRC

Order Information:
Web: http://www.ectj.org

Related research

Keywords:

Other versions of this item:

Find related papers by JEL classification:

References

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.:
as in new window
  1. Joshua D. Angrist & Jörn-Steffen Pischke, 2010. "The Credibility Revolution in Empirical Economics: How Better Research Design is Taking the Con out of Econometrics," Working Paper Series of the German Council for Social and Economic Data 142, German Council for Social and Economic Data (RatSWD).
  2. Peter C. B. Phillips, 2005. "A Remark on Bimodality and Weak Instrumentation in Structural Equation Estimation," Cowles Foundation Discussion Papers 1540, Cowles Foundation for Research in Economics, Yale University.
  3. Aviv Nevo & Adam M. Rosen, 2008. "Identification with Imperfect Instruments," NBER Working Papers 14434, National Bureau of Economic Research, Inc.
  4. Charles F. Manski & John V. Pepper, 2009. "More on monotone instrumental variables," Econometrics Journal, Royal Economic Society, vol. 12(s1), pages S200-S216, 01.
  5. Charles F. Manski & John V. Pepper, 2000. "Monotone Instrumental Variables, with an Application to the Returns to Schooling," Econometrica, Econometric Society, vol. 68(4), pages 997-1012, July.
  6. Peter C.B. Phillips, 2007. "Exact Distribution Theory in Structural Estimation with an Identity," Cowles Foundation Discussion Papers 1613, Cowles Foundation for Research in Economics, Yale University.
  7. Nelson, C. & Startz, R., 1988. "Some Furthere Results On The Exact Small Sample Properties Of The Instrumental Variable Estimator," Discussion Papers in Economics at the University of Washington 88-06, Department of Economics at the University of Washington.
  8. Kiviet, Jan F. & Niemczyk, Jerzy, 2007. "The asymptotic and finite sample distributions of OLS and simple IV in simultaneous equations," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3296-3318, April.
  9. Nelson, C. & Startz, R., 1988. "The Distribution Of The Instrumental Variables Estimator And Its T-Ratio When The Instrument Is A Poor One," Discussion Papers in Economics at the University of Washington 88-07, Department of Economics at the University of Washington.
  10. Hillier, Grant, 2006. "Yet More On The Exact Properties Of Iv Estimators," Econometric Theory, Cambridge University Press, vol. 22(05), pages 913-931, October.
  11. Douglas Staiger & James H. Stock, 1994. "Instrumental Variables Regression with Weak Instruments," NBER Technical Working Papers 0151, National Bureau of Economic Research, Inc.
  12. Kiviet, Jan F. & Phillips, Garry D. A., 1996. "The bias of the ordinary least squares estimator in simultaneous equation models," Economics Letters, Elsevier, vol. 53(2), pages 161-167, November.
  13. Hu, Yingyao, 2006. "Bounding parameters in a linear regression model with a mismeasured regressor using additional information," Journal of Econometrics, Elsevier, vol. 133(1), pages 51-70, July.
  14. Timothy G. Conley & Christian B. Hansen & Peter E. Rossi, 2012. "Plausibly Exogenous," The Review of Economics and Statistics, MIT Press, vol. 94(1), pages 260-272, February.
  15. Alfonso Flores-Lagunes, 2007. "Finite sample evidence of IV estimators under weak instruments," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(3), pages 677-694.
  16. Aart Kraay, 2012. "Instrumental variables regressions with uncertain exclusion restrictions: a Bayesian approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(1), pages 108-128, 01.
  17. Maddala, G S & Jeong, Jinook, 1992. "On the Exact Small Sample Distribution of the Instrumental Variable Estimator," Econometrica, Econometric Society, vol. 60(1), pages 181-83, January.
  18. Erickson, Timothy, 1993. "Restricting Regression Slopes in the Errors-in-Variables Model by Bounding the Error Correlation," Econometrica, Econometric Society, vol. 61(4), pages 959-69, July.
  19. Donald, Stephen G & Newey, Whitney K, 2001. "Choosing the Number of Instruments," Econometrica, Econometric Society, vol. 69(5), pages 1161-91, September.
  20. Mariano, Roberto S, 1973. "Approximations to the Distribution Functions of the Ordinary Least-Squares and Two-Stage Least-Squares Estimators in the Case of Two Included Endogenous Variables," Econometrica, Econometric Society, vol. 41(1), pages 67-77, January.
  21. Giovanni Forchini, 2005. "On the Bimodality of the Exact Distribution of the TSLS Estimator," Monash Econometrics and Business Statistics Working Papers 14/05, Monash University, Department of Econometrics and Business Statistics.
  22. Andrews, Donald W.K. & Stock, James H., 2007. "Testing with many weak instruments," Journal of Econometrics, Elsevier, vol. 138(1), pages 24-46, May.
  23. Jinyong Hahn & Jerry Hausman, 2003. "Weak Instruments: Diagnosis and Cures in Empirical Econometrics," American Economic Review, American Economic Association, vol. 93(2), pages 118-125, May.
Full references (including those not matched with items on IDEAS)

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as in new window

Cited by:
  1. Firmin Doko Tchatoka & Jean-Marie Dufour, 2014. "Identification-robust inference for endogeneity parameters in linear structural models," CIRANO Working Papers 2014s-17, CIRANO.
  2. Denizer, Cevdet & Kaufmann, Daniel & Kraay, Aart, 2013. "Good countries or good projects? Macro and micro correlates of World Bank project performance," Journal of Development Economics, Elsevier, vol. 105(C), pages 288-302.
  3. Skeels, Christopher L. & Taylor, Larry W., 2014. "Prediction after IV estimation," Economics Letters, Elsevier, vol. 122(3), pages 420-422.
  4. Bun, Maurice J.G. & Harrison, Teresa D., 2014. "OLS and IV estimation of regression models including endogenous interaction terms," School of Economics Working Paper Series 2014-3, LeBow College of Business, Drexel University.

Lists

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

Statistics

Access and download statistics

Corrections

When requesting a correction, please mention this item's handle: RePEc:wly:emjrnl:v:16:y:2013:i:1:p:s24-s59. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Wiley-Blackwell Digital Licensing) or (Christopher F. Baum).

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.