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Cross-sectional averaging and instrumental variable estimation with many weak instruments

  • Kapetanios, George
  • Marcellino, Massimiliano

The present paper suggests a new way to carry out IV estimation with many instruments. Our suggestion is to cross-sectionally average the instruments and use these averages as instruments. We provide a theoretical and Monte Carlo analysis of this approach.

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File URL: http://www.sciencedirect.com/science/article/B6V84-4YTN3TY-1/2/81148b4705cfa73251b8406f1c433e95
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Article provided by Elsevier in its journal Economics Letters.

Volume (Year): 108 (2010)
Issue (Month): 1 (July)
Pages: 36-39

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Handle: RePEc:eee:ecolet:v:108:y:2010:i:1:p:36-39
Contact details of provider: Web page: http://www.elsevier.com/locate/ecolet

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  1. repec:att:wimass:9220 is not listed on IDEAS
  2. Chao, John Chao & Norman R. Swanson, 2003. "Consistent Estimation with a Large Number of Weak Instruments," Cowles Foundation Discussion Papers 1417, Cowles Foundation for Research in Economics, Yale University.
  3. Bekker, Paul A, 1994. "Alternative Approximations to the Distributions of Instrumental Variable Estimators," Econometrica, Econometric Society, vol. 62(3), pages 657-81, May.
  4. Hahn, Jinyong & Kuersteiner, Guido, 2002. "Discontinuities of weak instrument limiting distributions," Economics Letters, Elsevier, vol. 75(3), pages 325-331, May.
  5. 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.
  6. Beyer, Andreas & Farmer, Roger E. A., 2004. "On the indeterminacy of new-Keynesian economics," Working Paper Series 0323, European Central Bank.
  7. Donald, Stephen G & Newey, Whitney K, 2001. "Choosing the Number of Instruments," Econometrica, Econometric Society, vol. 69(5), pages 1161-91, September.
  8. Carlo Ambrogio Favero & Massimilano Marcellino & Francesca Neglia, . "Principal components at work: The empirical analysis of monetary policy with large datasets," Working Papers 223, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  9. 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.).
  10. John Shea, 1996. "Instrument Relevance in Multivariate Linear Models: A Simple Measure," NBER Technical Working Papers 0193, National Bureau of Economic Research, Inc.
  11. Newey, Whitney K & West, Kenneth D, 1994. "Automatic Lag Selection in Covariance Matrix Estimation," Review of Economic Studies, Wiley Blackwell, vol. 61(4), pages 631-53, October.
  12. Douglas Staiger & James H. Stock, 1994. "Instrumental Variables Regression with Weak Instruments," NBER Technical Working Papers 0151, National Bureau of Economic Research, Inc.
  13. Rothenberg, Thomas J., 1984. "Approximating the distributions of econometric estimators and test statistics," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 15, pages 881-935 Elsevier.
  14. D.S. Poskitt & C.L. Skeels, 2002. "Assessing Instrumental Variable Relevance:An Alternative Measure and Some Exact Finite Sample Theory," Department of Economics - Working Papers Series 862, The University of Melbourne.
  15. Beyer, Andreas & Farmer, Roger E A & Henry, Jérôme & Marcellino, Massimiliano, 2005. "Factor Analysis in a New-Keynesian Model," CEPR Discussion Papers 5266, C.E.P.R. Discussion Papers.
  16. 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.
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