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Semiparametric Estimation of Index Coefficients
Abstract
This paper gives a solution to the problem of estimating coefficients of index models, through the estimation of the density-weighted average derivative of a general regression function. A normalized version of the density-weighted average derivative can be estimated by certain linear instrumental variables coefficients. The estimators, based on sample analogies of the product moment representation of the average derivative, are constructed using nonparametric kernel estimators of the density of the regressors. Consistent estimators of the asymptotic variance-covariance matrices of the estimators are given, and a limited Monte Carlo simulation is used to study the practical performance of the procedures. Copyright 1989 by The Econometric Society.
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Cited by
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Heckman, J. J., Ichimura, H., & Todd, P. (1998) · Review of Economic Studies · doi:10.1111/1467-937x.00044 · Cited by 3,807
Chapter 36 Large sample estimation and hypothesis testing
Newey, W. K., & McFadden, D. (1994) · Handbook of Econometrics · doi:10.1016/s1573-4412(05)80005-4 · Cited by 1,743
Empirical Strategies in Labor Economics
Angrist, J. D., & Krueger, A. B. (1999) · Handbook of Labor Economics · doi:10.1016/s1573-4463(99)03004-7 · Cited by 1,628
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Winship, C., & Mare, R. D. (1992) · Annual Review of Sociology · doi:10.1146/annurev.so.18.080192.001551 · Cited by 812
Metadata
- Authors
- Powell, J. L., Stock, J. H., & Stoker, T. M.
- Venue
- Econometrica
- Publisher
- JSTOR
- Published
- 1989-11
- Volume
- 57
- Issue
- 6
- Pages
- 1403
- ISSN
- 0012-9682 (ISSN-L)
- OpenAlex
- W2033148801
- Cited by
- 936
- References
- 0
- FWCI
- 8.27
- Type
- journal article
- Language
- es
- Open access
- closed
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