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Instrument Relevance in Multivariate Linear Models: A Simple Measure

  • John Shea

The correlation between instruments and explanatory variables is a key determinant of the performance of the instrumental variables estimator. The R 2 from regressing the explanatory variable on the instrument vector is a useful measure of relevance in univariate models, but can be misleading when there are multiple endogenous variables. This note proposes a computationally simple partial R 2 measure of instrument relevance for multivariate models. © 1997 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology

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File URL: http://www.mitpressjournals.org/doi/pdf/10.1162/rest.1997.79.2.348
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Article provided by MIT Press in its journal The Review of Economics and Statistics.

Volume (Year): 79 (1997)
Issue (Month): 2 (May)
Pages: 348-352

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Handle: RePEc:tpr:restat:v:79:y:1997:i:2:p:348-352
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  1. Charles R. Nelson & Richard Startz, 1988. "The Distribution of the Instrumental Variables Estimator and Its t-RatioWhen the Instrument is a Poor One," NBER Technical Working Papers 0069, National Bureau of Economic Research, Inc.
  2. Joshua Angrist & Alan Krueger, 1993. "Split Sample Instrumental Variables," Working Papers 699, Princeton University, Department of Economics, Industrial Relations Section..
  3. John Y. Campbell & N. Gregory Mankiw, 1991. "Permanent Income, Current Income, and Consumption," NBER Working Papers 2436, National Bureau of Economic Research, Inc.
  4. Fuhrer, Jeffrey C. & Moore, George R. & Schuh, Scott D., 1995. "Estimating the linear-quadratic inventory model Maximum likelihood versus generalized method of moments," Journal of Monetary Economics, Elsevier, vol. 35(1), pages 115-157, February.
  5. Miron, Jeffrey A & Zeldes, Stephen P, 1988. "Seasonality, Cost Shocks, and the Production Smoothing Models of Inventories," Econometrica, Econometric Society, vol. 56(4), pages 877-908, July.
  6. 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.).
  7. Caballero, R.J. & Lyons, R.K., 1991. "External Effects in U.S. Procyclical Productivity," Papers 91-19, Columbia - Graduate School of Business.
  8. 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.
  9. Buse, A, 1992. "The Bias of Instrumental Variable Estimators," Econometrica, Econometric Society, vol. 60(1), pages 173-80, January.
  10. Shea, J., 1991. "Do Supply Curves Slope Up?," Working papers 9116, Wisconsin Madison - Social Systems.
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