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Instrument Relevance in Multivariate Linear Models: A Simple Measure

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  • John Shea

Abstract

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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Bibliographic Info

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. Alastair R. Hall & Glenn D. Rudebusch & David W. Wilcox, 1994. "Judging instrument relevance in instrumental variables estimation," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.) 94-3, Board of Governors of the Federal Reserve System (U.S.).
  2. Jeffrey Fuhrer & George Moore & Scott Schuh, 1993. "Estimating the linear-quadratic inventory model: maximum likelihood versus generalized method of moments," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.) 93-11, Board of Governors of the Federal Reserve System (U.S.).
  3. Shea, J., 1991. "Do Supply Curves Slope Up?," Working papers, Wisconsin Madison - Social Systems 9116, Wisconsin Madison - Social Systems.
  4. 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, Department of Economics at the University of Washington 88-07, Department of Economics at the University of Washington.
  5. 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, American Statistical Association, vol. 13(2), pages 225-35, April.
  6. John Y. Campbell & N. Gregory Mankiw, 1991. "Permanent Income, Current Income, and Consumption," NBER Working Papers 2436, National Bureau of Economic Research, Inc.
  7. Caballero, Ricardo J. & Lyons, Richard K., 1992. "External effects in U.S. procyclical productivity," Journal of Monetary Economics, Elsevier, Elsevier, vol. 29(2), pages 209-225, April.
  8. Joshua D. Angrist & Alan B. Krueger, 1995. "Split Sample Instrumental Variables," NBER Technical Working Papers, National Bureau of Economic Research, Inc 0150, National Bureau of Economic Research, Inc.
  9. Buse, A, 1992. "The Bias of Instrumental Variable Estimators," Econometrica, Econometric Society, Econometric Society, vol. 60(1), pages 173-80, January.
  10. Jeffrey A. Miron & Stephen P. Zeldes, . "Seasonality, Cost Shocks and the Production Smoothing Model of Inventories," Rodney L. White Center for Financial Research Working Papers, Wharton School Rodney L. White Center for Financial Research 01-87, Wharton School Rodney L. White Center for Financial Research.
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