Nonparametric Instrumental Variable Estimation in Practice
AbstractIn this paper we examine the finite sample performance of two estimators one developed by Blundell, Chen, and Kristensen (2007) (BCK) and the other by Gagliardini and Scaillet (2007) (TIR). This paper focuses on the generalization and expansion of these estimators to a full nonparametric specification with multiple regressors. In relation to the classic weak instruments literature, we provide intuition on the examination of instruments relevance when the structural function is assumed to be unknown. Simulations indicate that both estimators perform quite well in higher dimensions. This research also provides insights on the performance of bootstrapped confidence intervals for both estimators. We document that the BCK estimator's coverage probabilities are near their nominal levels even in small samples as long as the sieve order of expansion is restricted. The coverage probability for the TIR estimator's bootstrapped confidence intervals are near their nominal levels even when the order of sieve approximation is large. These results suggest that in small samples the TIR estimator has a much smaller bias then the BCK estimator but its variance is much larger. We provide two empirical examples. One is the classic wage returns to education example and the other looks at the relationship of corruption and GDP to economic growth. Results here suggests that the impact of corruption on growth depends nonlinearly on a countries level of development.
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Bibliographic InfoPaper provided by University of Connecticut, Food Marketing Policy Center in its series Research Reports with number 149936.
Date of creation: Nov 2008
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More information through EDIRC
Nonparametric; Instrumental Variables; Information Regularized Estimators; Research Methods/ Statistical Methods; C13; C14; C15;
Find related papers by JEL classification:
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Whitney K. Newey & James L. Powell, 2003. "Instrumental Variable Estimation of Nonparametric Models," Econometrica, Econometric Society, vol. 71(5), pages 1565-1578, 09.
- Mauro, Paolo, 1995. "Corruption and Growth," The Quarterly Journal of Economics, MIT Press, vol. 110(3), pages 681-712, August.
- Severini, Thomas A. & Tripathi, Gautam, 2006.
"Some Identification Issues In Nonparametric Linear Models With Endogenous Regressors,"
Cambridge University Press, vol. 22(02), pages 258-278, April.
- Thomas A. Severini & Gautam Tripathi, 2005. "Some Identification Issues in Nonparametric Linear Models with Endogenous Regressors," Working papers 2005-12, University of Connecticut, Department of Economics.
- Douglas Staiger & James H. Stock, 1997.
"Instrumental Variables Regression with Weak Instruments,"
Econometric Society, vol. 65(3), pages 557-586, May.
- Douglas Staiger & James H. Stock, 1994. "Instrumental Variables Regression with Weak Instruments," NBER Technical Working Papers 0151, National Bureau of Economic Research, Inc.
- Philip Shaw & Marina-Selini Katsaiti & Marius Jurgilas, 2006.
"Corruption and Growth Under Weak Identification,"
2006-17, University of Connecticut, Department of Economics, revised Mar 2007.
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