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Pairwise‐comparison estimation with non‐parametric controls

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  • Koen Jochmans

Abstract

The purpose of this paper is the presentation of distribution theory for generic estimators based on the pairwise comparison of observations in problems where identification is achieved through the use of control functions. The controls can be specified semi- or non-parametrically. The criterion function may be non-smooth. The theory is applied to the estimation of the coefficients in a monotone linear-index model and to inference on the link function in a partially-linear transformation model. A number of simulation exercises serve to assess the small-sample performance of these techniques.

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File URL: http://hdl.handle.net/10.1111/ectj.2013.16.issue-3
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Bibliographic Info

Article provided by Royal Economic Society in its journal Econometrics Journal.

Volume (Year): 16 (2013)
Issue (Month): 3 (October)
Pages: 340-372

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Handle: RePEc:wly:emjrnl:v:16:y:2013:i:3:p:340-372

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References

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  1. Hidehiko Ichimura & Sokbae 'Simon' Lee, 2006. "Characterization of the asymptotic distribution of semiparametric M-estimators," CeMMAP working papers CWP15/06, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
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  4. Andres Aradillas-Lopez & Bo E. Honoré & James L. Powell, 2007. "Pairwise Difference Estimation With Nonparametric Control Variables," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 48(4), pages 1119-1158, November.
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  7. Victor Chernozhukov & Ivan Fernandez-Val & Alfred Galichon, 2008. "Improving point and interval estimates of monotone functions by rearrangement," CeMMAP working papers CWP17/08, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  8. Sherman, Robert P., 1994. "U-Processes in the Analysis of a Generalized Semiparametric Regression Estimator," Econometric Theory, Cambridge University Press, vol. 10(02), pages 372-395, June.
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  10. Songnian Chen, 2002. "Rank Estimation of Transformation Models," Econometrica, Econometric Society, vol. 70(4), pages 1683-1697, July.
  11. Jason Abrevaya & Jerry A. Hausman & Shakeeb Khan, 2010. "Testing for Causal Effects in a Generalized Regression Model With Endogenous Regressors," Econometrica, Econometric Society, vol. 78(6), pages 2043-2061, November.
  12. Ahn, Hyungtaik & Powell, James L., 1993. "Semiparametric estimation of censored selection models with a nonparametric selection mechanism," Journal of Econometrics, Elsevier, vol. 58(1-2), pages 3-29, July.
  13. Khan, Shakeeb, 2001. "Two-stage rank estimation of quantile index models," Journal of Econometrics, Elsevier, vol. 100(2), pages 319-355, February.
  14. Richard Blundell & James Powell, 2001. "Endogeneity in semiparametric binary response models," CeMMAP working papers CWP05/01, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  15. Jason Abrevaya & Youngki Shin, 2011. "Rank estimation of partially linear index models," Econometrics Journal, Royal Economic Society, vol. 14(3), pages 409-437, October.
  16. Koen Jochmans, 2011. "The variance of a rank estimator of transformation models," Sciences Po publications info:hdl:2441/7nu0cp0n9on, Sciences Po.
  17. Robinson, Peter M, 1988. "Root- N-Consistent Semiparametric Regression," Econometrica, Econometric Society, vol. 56(4), pages 931-54, July.
  18. Sherman, Robert P, 1993. "The Limiting Distribution of the Maximum Rank Correlation Estimator," Econometrica, Econometric Society, vol. 61(1), pages 123-37, January.
  19. Honore, Bo E. & Powell, James L., 1994. "Pairwise difference estimators of censored and truncated regression models," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 241-278.
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Cited by:
  1. Koen Jochmans, 2011. "Identification in Bivariate binary-choice Models with elliptical innovations," Sciences Po publications info:hdl:2441/eu4vqp9ompq, Sciences Po.

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