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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-squared 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 paper proposes a computationally simple partial R- squared measure of instrument relevance for multivariate models.

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Bibliographic Info

Paper provided by National Bureau of Economic Research, Inc in its series NBER Technical Working Papers with number 0193.

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Date of creation: Mar 1996
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Publication status: published as Review of Economics and Statistics, Vol. 79, no. 2 (May 1997): 348-352.
Handle: RePEc:nbr:nberte:0193

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  1. 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 01-87, Wharton School Rodney L. White Center for Financial Research.
  2. 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.
  3. Hall, Alastair R & Rudebusch, Glenn D & Wilcox, David W, 1996. "Judging Instrument Relevance in Instrumental Variables Estimation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 37(2), pages 283-98, May.
  4. Caballero, R.J. & Lyons, R.K., 1991. "External Effects in U.S. Procyclical Productivity," Papers 91-19, Columbia - Graduate School of Business.
  5. repec:att:wimass:9116 is not listed on IDEAS
  6. 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.
  7. Joshua Angrist & Alan Krueger, 1993. "Split Sample Instrumental Variables," Working Papers 699, Princeton University, Department of Economics, Industrial Relations Section..
  8. John Y. Campbell & N. Gregory Mankiw, 1991. "Permanent Income, Current Income, and Consumption," NBER Working Papers 2436, National Bureau of Economic Research, Inc.
  9. Shea, John, 1993. "Do Supply Curves Slope Up?," The Quarterly Journal of Economics, MIT Press, vol. 108(1), pages 1-32, February.
  10. 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.
  11. Buse, A, 1992. "The Bias of Instrumental Variable Estimators," Econometrica, Econometric Society, vol. 60(1), pages 173-80, January.
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