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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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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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Handle: RePEc:nbr:nberte:0193

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C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General

References listed on IDEAS
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  1. 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. [Downloadable!] (restricted)
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  2. Nelson, Charles R & Startz, Richard, 1990. "The Distribution of the Instrumental Variables Estimator and Its t-Ratio When the Instrument Is a Poor One," Journal of Business, University of Chicago Press, vol. 63(1), pages S125-40, January. [Downloadable!] (restricted)
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  3. Shea, John, 1993. "Do Supply Curves Slope Up?," The Quarterly Journal of Economics, MIT Press, vol. 108(1), pages 1-32, February. [Downloadable!] (restricted)
  4. Campbell, John Y & Mankiw, N Gregory, 1990. "Permanent Income, Current Income, and Consumption," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(3), pages 265-79, July.
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  5. Buse, A, 1992. "The Bias of Instrumental Variable Estimators," Econometrica, Econometric Society, vol. 60(1), pages 173-80, January. [Downloadable!] (restricted)
  6. 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.
  7. Joshua D. Angrist & Alan B. Krueger, 1995. "Split Sample Instrumental Variables," NBER Technical Working Papers 0150, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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  8. 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. [Downloadable!] (restricted)
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  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.).
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  10. Caballero, Ricardo J. & Lyons, Richard K., 1992. "External effects in U.S. procyclical productivity," Journal of Monetary Economics, Elsevier, vol. 29(2), pages 209-225, April. [Downloadable!] (restricted)
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  3. Michael Bleaney & Arcangelo Dimico, . "Biogeographical Conditions, the Transition to Agriculture and Long-Run Growth," Discussion Papers 08/15, University of Nottingham, CREDIT. [Downloadable!]
  4. Mary Amiti & Jozef Konings, 2005. "Trade Liberalization, Intermediate Inputs, and Productivity: Evidence from Indonesia," IMF Working Papers 05/146, International Monetary Fund. [Downloadable!]
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  7. Bolaky, Bineswaree & Freund, Caroline, 2004. "Trade, regulations, and growth," Policy Research Working Paper Series 3255, The World Bank. [Downloadable!]
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